{"as_of":"2026-08-19T19:49:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:6abd459f9141a7d4a022ec1ab4b442be11ce9ac75f4d2fb69834a817a4c05b4d","coverage":[{"denominator":98,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":98,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-15T18:46:46.283585Z","state":"measured"},{"denominator":106,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":106,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-19T06:32:44.657259+00:00","state":"measured"},{"denominator":8,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":8,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-04T08:45:16.299541Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-07-01T17:05:50.613000Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2506.18890","last_updated":"2025-06-23T17:57:47Z","snapshot_observed_at":"2026-08-19T17:54:23.387474Z","submitted_at":"2025-06-23T17:57:47Z","title":"4D-LRM: Large Space-Time Reconstruction Model From and To Any View at Any Time","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2506.18890","snapshot_observed_at":"2026-08-04T08:45:16.299541Z","title":": 4d-lrm: Large space-time reconstruction model from and to any view at any time","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2510.19255","last_updated":"2026-06-15T21:42:55Z","snapshot_observed_at":"2026-08-18T20:27:14.700404Z","submitted_at":"2025-10-22T05:22:20Z","title":"Advances in 4D Representation: Geometry, Motion, and Interaction","version":3},"reference_index":205,"source":"arxiv_source","source_observed_at":"2026-08-04T08:45:16.299541Z"},"links":{"cited_paper":"/paper/2506.18890","citing_paper":"/paper/2510.19255"},"observation_digest":"sha256:21c993d3d23c9d94c5fa894b6781f9e5cbe383d34d038f7183d7760daf173379","observation_id":"9fd8a130-2591-408d-ae76-cb222451b105","resolution":{"observed_at":"2026-08-04T08:45:16.299541Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2506.18890","last_updated":"2025-06-23T17:57:47Z","snapshot_observed_at":"2026-08-19T17:54:23.387474Z","submitted_at":"2025-06-23T17:57:47Z","title":"4D-LRM: Large Space-Time Reconstruction Model From and To Any View at Any Time","version":1},"cited_work":{"arxiv_id":"2506.18890","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2506.18890","snapshot_observed_at":"2026-07-01T17:05:50.613000Z","title":"arXiv preprint arXiv:2506.18890 (2025)","venue":null,"work_id":"59ebec2c-a8a3-49ff-acf7-e30508251b6b","year":2025},"citing_paper":{"arxiv_id":"2604.05182","last_updated":"2026-07-22T21:33:06Z","snapshot_observed_at":"2026-08-02T16:46:05.370757Z","submitted_at":"2026-04-06T21:21:12Z","title":"LSRM: High-Fidelity Object-Centric Reconstruction via Scaled Context Windows","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-05-10T19:32:20.565326Z"},"links":{"cited_paper":"/paper/2506.18890","citing_paper":"/paper/2604.05182"},"observation_digest":"sha256:b6ecb15225e3a33bbda4c3d4299d5ab7cdc468640a9fc7eafe085159c64f989a","observation_id":"fab77974-8f63-40ce-9536-bd783694dddf","resolution":{"observed_at":"2026-05-10T22:50:49.732130Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2506.18890","last_updated":"2025-06-23T17:57:47Z","snapshot_observed_at":"2026-08-19T17:54:23.387474Z","submitted_at":"2025-06-23T17:57:47Z","title":"4D-LRM: Large Space-Time Reconstruction Model From and To Any View at Any Time","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2506.18890","snapshot_observed_at":"2026-07-13T09:33:39.204257Z","title":"arXiv preprint arXiv:2506.18890 (2025)","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2604.05182","last_updated":"2026-07-22T21:33:06Z","snapshot_observed_at":"2026-08-02T16:46:05.370757Z","submitted_at":"2026-04-06T21:21:12Z","title":"LSRM: High-Fidelity Object-Centric Reconstruction via Scaled Context Windows","version":2},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-07-13T09:33:39.204257Z"},"links":{"cited_paper":"/paper/2506.18890","citing_paper":"/paper/2604.05182"},"observation_digest":"sha256:4f7a458bf1c9f73fcb381401b50e1da488f94dcae2397ee56973a30a9f09da7d","observation_id":"73e79da8-f552-4c40-ba9f-f42fbc5e3a43","resolution":{"observed_at":"2026-07-13T09:33:39.204257Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2506.18890","last_updated":"2025-06-23T17:57:47Z","snapshot_observed_at":"2026-08-19T17:54:23.387474Z","submitted_at":"2025-06-23T17:57:47Z","title":"4D-LRM: Large Space-Time Reconstruction Model From and To Any View at Any Time","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2506.18890","snapshot_observed_at":"2026-08-02T16:46:06.552162Z","title":"arXiv preprint arXiv:2506.18890 (2025)","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2604.05182","last_updated":"2026-07-22T21:33:06Z","snapshot_observed_at":"2026-08-02T16:46:05.370757Z","submitted_at":"2026-04-06T21:21:12Z","title":"LSRM: High-Fidelity Object-Centric Reconstruction via Scaled Context Windows","version":3},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-02T16:46:06.552162Z"},"links":{"cited_paper":"/paper/2506.18890","citing_paper":"/paper/2604.05182"},"observation_digest":"sha256:14eca858ad62d4ad96e1d9b49af6cc21104443aa1901fed0c1283bc1c53179c5","observation_id":"cb32a12d-5c4e-4fbe-affb-78c41fd6261d","resolution":{"observed_at":"2026-08-02T16:46:06.552162Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2506.18890","last_updated":"2025-06-23T17:57:47Z","snapshot_observed_at":"2026-08-19T17:54:23.387474Z","submitted_at":"2025-06-23T17:57:47Z","title":"4D-LRM: Large Space-Time Reconstruction Model From and To Any View at Any Time","version":1},"cited_work":{"arxiv_id":"2506.18890","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2506.18890","snapshot_observed_at":"2026-07-01T17:05:50.613000Z","title":"arXiv preprint arXiv:2506.18890 (2025)","venue":null,"work_id":"59ebec2c-a8a3-49ff-acf7-e30508251b6b","year":2025},"citing_paper":{"arxiv_id":"2604.14025","last_updated":"2026-04-15T16:07:18Z","snapshot_observed_at":"2026-07-06T23:01:55.698452Z","submitted_at":"2026-04-15T16:07:18Z","title":"Feed-Forward 3D Scene Modeling: A Problem-Driven Perspective","version":1},"reference_index":190,"source":"pdf_text","source_observed_at":"2026-05-10T13:13:42.052386Z"},"links":{"cited_paper":"/paper/2506.18890","citing_paper":"/paper/2604.14025"},"observation_digest":"sha256:555c082fc19645fbc4f517bccfd8cdd44c9d576f87c53dde96eabe3dbba8bf7f","observation_id":"cfa5ad26-de76-4778-a180-06cba7a29edb","resolution":{"observed_at":"2026-05-10T13:20:26.086626Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2506.18890","last_updated":"2025-06-23T17:57:47Z","snapshot_observed_at":"2026-08-19T17:54:23.387474Z","submitted_at":"2025-06-23T17:57:47Z","title":"4D-LRM: Large Space-Time Reconstruction Model From and To Any View at Any Time","version":1},"cited_work":{"arxiv_id":"2506.18890","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2506.18890","snapshot_observed_at":"2026-07-01T17:05:50.613000Z","title":"arXiv preprint arXiv:2506.18890 (2025)","venue":null,"work_id":"59ebec2c-a8a3-49ff-acf7-e30508251b6b","year":2025},"citing_paper":{"arxiv_id":"2605.31595","last_updated":"2026-05-29T17:57:41Z","snapshot_observed_at":"2026-07-31T18:19:26.820130Z","submitted_at":"2026-05-29T17:57:41Z","title":"Learning Global Motion with Compact Gaussians for Feed-Forward 4D Reconstruction","version":1},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-06-28T23:07:50.491677Z"},"links":{"cited_paper":"/paper/2506.18890","citing_paper":"/paper/2605.31595"},"observation_digest":"sha256:40a860ca930e23a20b92dc7c941a586908817d6273ba2f3d3d60882b6a10cb98","observation_id":"7a2a05f0-44d6-439b-a631-cd198bdc1e65","resolution":{"observed_at":"2026-06-28T23:12:47.242728Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2506.18890","last_updated":"2025-06-23T17:57:47Z","snapshot_observed_at":"2026-08-19T17:54:23.387474Z","submitted_at":"2025-06-23T17:57:47Z","title":"4D-LRM: Large Space-Time Reconstruction Model From and To Any View at Any Time","version":1},"cited_work":{"arxiv_id":"2506.18890","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2506.18890","snapshot_observed_at":"2026-07-01T17:05:50.613000Z","title":"arXiv preprint arXiv:2506.18890 (2025)","venue":null,"work_id":"59ebec2c-a8a3-49ff-acf7-e30508251b6b","year":2025},"citing_paper":{"arxiv_id":"2606.28215","last_updated":"2026-06-26T16:05:58Z","snapshot_observed_at":"2026-08-07T08:50:21.361337Z","submitted_at":"2026-06-26T16:05:58Z","title":"HAT-4D: Lifting Monocular Video for 4D Multi-Object Interactions via Human-Agent Collaboration","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-06-29T04:18:02.341742Z"},"links":{"cited_paper":"/paper/2506.18890","citing_paper":"/paper/2606.28215"},"observation_digest":"sha256:ea04e922dfbd98fc391e1391247d192099f35beaf3cb5da9820611ffd5bada91","observation_id":"f109f5f8-4a33-4075-81a7-f092cafa6753","resolution":{"observed_at":"2026-07-01T17:05:50.615390Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2506.18890","last_updated":"2025-06-23T17:57:47Z","snapshot_observed_at":"2026-08-19T17:54:23.387474Z","submitted_at":"2025-06-23T17:57:47Z","title":"4D-LRM: Large Space-Time Reconstruction Model From and To Any View at Any Time","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2506.18890","snapshot_observed_at":"2026-08-01T04:24:27.135103Z","title":"4D-LRM : Large space-time reconstruction model from and to any view at any time","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.27634","last_updated":"2026-07-30T03:45:34Z","snapshot_observed_at":"2026-08-12T13:18:02.063666Z","submitted_at":"2026-07-30T03:45:34Z","title":"4DHumanDiff: Direct Text-to-4DGS Generation for Consistent 360-Degree Dynamic Humans","version":1},"reference_index":85,"source":"arxiv_source","source_observed_at":"2026-08-01T04:24:27.135103Z"},"links":{"cited_paper":"/paper/2506.18890","citing_paper":"/paper/2607.27634"},"observation_digest":"sha256:61c2a14b9e1f9e11c5b65232780cdc1d29cb627c51631e1a302d36255e695b50","observation_id":"257c6315-7ebb-45a1-94cc-22963ab78d35","resolution":{"observed_at":"2026-08-01T04:24:27.135103Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2506.18890/citation-record","integrity":"/paper/2506.18890/integrity","json":"/paper/2506.18890/citation-record.json","paper":"/paper/2506.18890"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:46:45.787549Z","title":"Building rome in a day.Communications of the ACM, 54(10):105–112, 2011","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2506.18890","last_updated":"2025-06-23T17:57:47Z","snapshot_observed_at":"2026-08-19T17:54:23.387474Z","submitted_at":"2025-06-23T17:57:47Z","title":"4D-LRM: Large Space-Time Reconstruction Model From and To Any View at Any Time","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-15T18:46:45.787549Z"},"links":{"citing_paper":"/paper/2506.18890"},"observation_digest":"sha256:f6d2ebe3718470ed76330e5f3da47659c00233618a92d2df89c794721fb0e0e9","observation_id":"0940e99f-2635-4cd6-84dd-a4441e0be03c","resolution":{"observed_at":"2026-08-15T18:46:45.787549Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1607.06450","last_updated":"2016-07-21T19:57:52Z","snapshot_observed_at":"2026-08-15T04:53:45.483331Z","submitted_at":"2016-07-21T19:57:52Z","title":"Layer Normalization","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1607.06450","snapshot_observed_at":"2026-08-15T18:46:45.793743Z","title":"Layer normalization.arXiv preprint arXiv:1607.06450, 2016","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2506.18890","last_updated":"2025-06-23T17:57:47Z","snapshot_observed_at":"2026-08-19T17:54:23.387474Z","submitted_at":"2025-06-23T17:57:47Z","title":"4D-LRM: Large Space-Time Reconstruction Model From and To Any View at Any Time","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-15T18:46:45.793743Z"},"links":{"cited_paper":"/paper/1607.06450","citing_paper":"/paper/2506.18890"},"observation_digest":"sha256:803023e7036fb68a6da5dfbf29381c2428cef95fcd9fb73f84d0858d07ccf6f3","observation_id":"fae2a3e3-0692-465d-80cd-a481d7b36569","resolution":{"observed_at":"2026-08-15T18:46:45.793743Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:46:45.799663Z","title":"Hexplane: Afastrepresentationfordynamicscenes","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.18890","last_updated":"2025-06-23T17:57:47Z","snapshot_observed_at":"2026-08-19T17:54:23.387474Z","submitted_at":"2025-06-23T17:57:47Z","title":"4D-LRM: Large Space-Time Reconstruction Model From and To Any View at Any Time","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-15T18:46:45.799663Z"},"links":{"citing_paper":"/paper/2506.18890"},"observation_digest":"sha256:a0cfdd2fe34efb2ea1a43eb4521370ab934caca602b7e31e47e4ad666ace05be","observation_id":"0ef6e83e-e804-4c69-8b38-db47aa8e4c42","resolution":{"observed_at":"2026-08-15T18:46:45.799663Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:46:45.804840Z","title":"Efficient geometry- aware 3d generative adversarial networks","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.18890","last_updated":"2025-06-23T17:57:47Z","snapshot_observed_at":"2026-08-19T17:54:23.387474Z","submitted_at":"2025-06-23T17:57:47Z","title":"4D-LRM: Large Space-Time Reconstruction Model From and To Any View at Any Time","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-15T18:46:45.804840Z"},"links":{"citing_paper":"/paper/2506.18890"},"observation_digest":"sha256:b247f33de76fa29cb3f414f9eb2f631f05f7427ae691a03e48a00f0d13ae366b","observation_id":"4133e0e7-8424-463f-89c7-f9708e9789ce","resolution":{"observed_at":"2026-08-15T18:46:45.804840Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:46:45.810050Z","title":"Hardware-constrained hybrid coding of video imagery","venue":null,"work_id":null,"year":1983},"citing_paper":{"arxiv_id":"2506.18890","last_updated":"2025-06-23T17:57:47Z","snapshot_observed_at":"2026-08-19T17:54:23.387474Z","submitted_at":"2025-06-23T17:57:47Z","title":"4D-LRM: Large Space-Time Reconstruction Model From and To Any View at Any Time","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-15T18:46:45.810050Z"},"links":{"citing_paper":"/paper/2506.18890"},"observation_digest":"sha256:9727e95e6c5a71d9542e4a6e48de13855dabe86b52bc947c2d07d813df43e82d","observation_id":"b84b6ced-95dc-4cd3-a83d-ab221d0ea5a9","resolution":{"observed_at":"2026-08-15T18:46:45.810050Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:46:45.815102Z","title":"pixelsplat: 3d gaussian splats from image pairs for scalable generalizable 3d reconstruction","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.18890","last_updated":"2025-06-23T17:57:47Z","snapshot_observed_at":"2026-08-19T17:54:23.387474Z","submitted_at":"2025-06-23T17:57:47Z","title":"4D-LRM: Large Space-Time Reconstruction Model From and To Any View at Any Time","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-15T18:46:45.815102Z"},"links":{"citing_paper":"/paper/2506.18890"},"observation_digest":"sha256:00218d791397ac74e0fbbd15a84acbea950c2c623793d3ae977041927f4bf1e1","observation_id":"27b9c869-53e2-49ef-8c4b-f6030bcfe956","resolution":{"observed_at":"2026-08-15T18:46:45.815102Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:46:45.820869Z","title":"Mvsnerf: Fastgeneralizableradiancefieldreconstructionfrommulti-viewstereo","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.18890","last_updated":"2025-06-23T17:57:47Z","snapshot_observed_at":"2026-08-19T17:54:23.387474Z","submitted_at":"2025-06-23T17:57:47Z","title":"4D-LRM: Large Space-Time Reconstruction Model From and To Any View at Any Time","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-15T18:46:45.820869Z"},"links":{"citing_paper":"/paper/2506.18890"},"observation_digest":"sha256:8c916e0d5da0e635a16f68948351ce1de5c8296237947856e7bfdb286360cabc","observation_id":"8a2ee073-afcb-4162-8783-a9f5d2eb5f3a","resolution":{"observed_at":"2026-08-15T18:46:45.820869Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:46:45.826168Z","title":"Tensorf: Tensorial radiance fields","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.18890","last_updated":"2025-06-23T17:57:47Z","snapshot_observed_at":"2026-08-19T17:54:23.387474Z","submitted_at":"2025-06-23T17:57:47Z","title":"4D-LRM: Large Space-Time Reconstruction Model From and To Any View at Any Time","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-15T18:46:45.826168Z"},"links":{"citing_paper":"/paper/2506.18890"},"observation_digest":"sha256:79e3844a7a302f56a20a2b2a62718b17931ab720d3a0936c89ee378ebc57318a","observation_id":"3da0d0f9-25f8-42a4-8eae-5383f4b5b746","resolution":{"observed_at":"2026-08-15T18:46:45.826168Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:46:45.831096Z","title":"Photographic image synthesis with cascaded refinement networks","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2506.18890","last_updated":"2025-06-23T17:57:47Z","snapshot_observed_at":"2026-08-19T17:54:23.387474Z","submitted_at":"2025-06-23T17:57:47Z","title":"4D-LRM: Large Space-Time Reconstruction Model From and To Any View at Any Time","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-15T18:46:45.831096Z"},"links":{"citing_paper":"/paper/2506.18890"},"observation_digest":"sha256:74970aaa5b1f2a04afc5ce438ee86f9af98cfb9cc459897d9db9b5772b399baf","observation_id":"67538d62-8c77-4821-bbd9-7f7a0f4e0b6a","resolution":{"observed_at":"2026-08-15T18:46:45.831096Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1604.06174","last_updated":"2016-04-22T19:21:36Z","snapshot_observed_at":"2026-08-14T14:33:36.303679Z","submitted_at":"2016-04-21T04:15:27Z","title":"Training Deep Nets with Sublinear Memory Cost","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1604.06174","snapshot_observed_at":"2026-08-15T18:46:45.836234Z","title":"Training deep nets with sublinear memory cost.arXiv preprint arXiv:1604.06174, 2016","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2506.18890","last_updated":"2025-06-23T17:57:47Z","snapshot_observed_at":"2026-08-19T17:54:23.387474Z","submitted_at":"2025-06-23T17:57:47Z","title":"4D-LRM: Large Space-Time Reconstruction Model From and To Any View at Any Time","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-15T18:46:45.836234Z"},"links":{"cited_paper":"/paper/1604.06174","citing_paper":"/paper/2506.18890"},"observation_digest":"sha256:a0d11048dbb50d0398bb7eb85d7924191e1af41d4fe6a59a63f1d7b392f7e0cf","observation_id":"f517f0b3-d514-4047-b614-39af702292cd","resolution":{"observed_at":"2026-08-15T18:46:45.836234Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:46:45.841939Z","title":"One-minute video generation with test-time training","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.18890","last_updated":"2025-06-23T17:57:47Z","snapshot_observed_at":"2026-08-19T17:54:23.387474Z","submitted_at":"2025-06-23T17:57:47Z","title":"4D-LRM: Large Space-Time Reconstruction Model From and To Any View at Any Time","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-15T18:46:45.841939Z"},"links":{"citing_paper":"/paper/2506.18890"},"observation_digest":"sha256:a77974f72daa6a0efc6d8341c48d3cba7219a6e36a78eaa75037693232d5122f","observation_id":"7486e9d4-913d-4b36-a4f0-c742ab514417","resolution":{"observed_at":"2026-08-15T18:46:45.841939Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:46:45.847050Z","title":"Flashattention-2: Faster attention with better parallelism and work partitioning","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.18890","last_updated":"2025-06-23T17:57:47Z","snapshot_observed_at":"2026-08-19T17:54:23.387474Z","submitted_at":"2025-06-23T17:57:47Z","title":"4D-LRM: Large Space-Time Reconstruction Model From and To Any View at Any Time","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-15T18:46:45.847050Z"},"links":{"citing_paper":"/paper/2506.18890"},"observation_digest":"sha256:f87e1ce442e044c90884419081701d3a441f4108a5972b139794a2862c6cb3bc","observation_id":"67c3f808-fc55-4e2e-a4a1-1cadd7cceaf3","resolution":{"observed_at":"2026-08-15T18:46:45.847050Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:46:45.852032Z","title":"Objaverse: A universe of annotated 3d objects","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.18890","last_updated":"2025-06-23T17:57:47Z","snapshot_observed_at":"2026-08-19T17:54:23.387474Z","submitted_at":"2025-06-23T17:57:47Z","title":"4D-LRM: Large Space-Time Reconstruction Model From and To Any View at Any Time","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-15T18:46:45.852032Z"},"links":{"citing_paper":"/paper/2506.18890"},"observation_digest":"sha256:79f637a5b30896e6138caf382485d0f823bb89a49842bf5279b04664d14cf431","observation_id":"899458a1-d8a1-48e2-bd1b-897e8d2b0ab1","resolution":{"observed_at":"2026-08-15T18:46:45.852032Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:46:45.856975Z","title":"Objaverse-xl: A universe of 10m+ 3d objects","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.18890","last_updated":"2025-06-23T17:57:47Z","snapshot_observed_at":"2026-08-19T17:54:23.387474Z","submitted_at":"2025-06-23T17:57:47Z","title":"4D-LRM: Large Space-Time Reconstruction Model From and To Any View at Any Time","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-15T18:46:45.856975Z"},"links":{"citing_paper":"/paper/2506.18890"},"observation_digest":"sha256:73263bc93eced8a449bfd61eaf1779049be6bd144bf8bcf5a30817a64ad742fc","observation_id":"35689c61-5ffc-46e8-be77-4e5ebf631868","resolution":{"observed_at":"2026-08-15T18:46:45.856975Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:46:45.861822Z","title":"Depth-supervised nerf: Fewer views and faster training for free","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.18890","last_updated":"2025-06-23T17:57:47Z","snapshot_observed_at":"2026-08-19T17:54:23.387474Z","submitted_at":"2025-06-23T17:57:47Z","title":"4D-LRM: Large Space-Time Reconstruction Model From and To Any View at Any Time","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-15T18:46:45.861822Z"},"links":{"citing_paper":"/paper/2506.18890"},"observation_digest":"sha256:ba527e81d3707498a77c304569f1af61a09db23cea00961452a82c4dbd681513","observation_id":"56262602-e9cb-4fe0-9f9c-30e6eb3158d0","resolution":{"observed_at":"2026-08-15T18:46:45.861822Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:46:45.866642Z","title":"An image is worth 16x16 words: Transformers for image recognition at scale","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2506.18890","last_updated":"2025-06-23T17:57:47Z","snapshot_observed_at":"2026-08-19T17:54:23.387474Z","submitted_at":"2025-06-23T17:57:47Z","title":"4D-LRM: Large Space-Time Reconstruction Model From and To Any View at Any Time","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-15T18:46:45.866642Z"},"links":{"citing_paper":"/paper/2506.18890"},"observation_digest":"sha256:62e0d7f22c636a0a19e65ce43b432c312874a6dc8615426443549943c1e0872c","observation_id":"eb034f44-b3e4-478e-953a-3f06b3bd082b","resolution":{"observed_at":"2026-08-15T18:46:45.866642Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:46:45.871566Z","title":"Google scanned objects: A high-quality dataset of 3d scanned household items","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.18890","last_updated":"2025-06-23T17:57:47Z","snapshot_observed_at":"2026-08-19T17:54:23.387474Z","submitted_at":"2025-06-23T17:57:47Z","title":"4D-LRM: Large Space-Time Reconstruction Model From and To Any View at Any Time","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-15T18:46:45.871566Z"},"links":{"citing_paper":"/paper/2506.18890"},"observation_digest":"sha256:0a49f9b24d72d7e31ce34c3fb9141dec0677a44d35b38612a0fd85f4a63251a3","observation_id":"dcbaaf82-bbc1-4257-bc24-7d3062249d19","resolution":{"observed_at":"2026-08-15T18:46:45.871566Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:46:45.876280Z","title":"Deformgs: Scene flow in highly deformable scenes for deformable object manipulation","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.18890","last_updated":"2025-06-23T17:57:47Z","snapshot_observed_at":"2026-08-19T17:54:23.387474Z","submitted_at":"2025-06-23T17:57:47Z","title":"4D-LRM: Large Space-Time Reconstruction Model From and To Any View at Any Time","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-15T18:46:45.876280Z"},"links":{"citing_paper":"/paper/2506.18890"},"observation_digest":"sha256:14d40393b569494f8242e8abea81631d5f6c205ca3e25c3a6f8193a703b6c9f5","observation_id":"2f2ec8fc-ceb2-4f33-9f79-217ff2afd8f5","resolution":{"observed_at":"2026-08-15T18:46:45.876280Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2504.13152","last_updated":"2025-04-17T17:55:58Z","snapshot_observed_at":"2026-08-18T18:17:05.102455Z","submitted_at":"2025-04-17T17:55:58Z","title":"St4RTrack: Simultaneous 4D Reconstruction and Tracking in the World","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2504.13152","snapshot_observed_at":"2026-08-15T18:46:45.881830Z","title":"St4rtrack: Simultaneous 4d reconstruction and tracking in the world.arXiv preprint arXiv:2504.13152, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.18890","last_updated":"2025-06-23T17:57:47Z","snapshot_observed_at":"2026-08-19T17:54:23.387474Z","submitted_at":"2025-06-23T17:57:47Z","title":"4D-LRM: Large Space-Time Reconstruction Model From and To Any View at Any Time","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-15T18:46:45.881830Z"},"links":{"cited_paper":"/paper/2504.13152","citing_paper":"/paper/2506.18890"},"observation_digest":"sha256:1e7436e4530a92a4a035522ff474461f3072adb82a314fdbaee1ae18798f4344","observation_id":"c39224ce-7974-40f5-9efc-fdca41d4b087","resolution":{"observed_at":"2026-08-15T18:46:45.881830Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:46:45.887314Z","title":"K-planes: Explicit radiance fields in space, time, and appearance","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.18890","last_updated":"2025-06-23T17:57:47Z","snapshot_observed_at":"2026-08-19T17:54:23.387474Z","submitted_at":"2025-06-23T17:57:47Z","title":"4D-LRM: Large Space-Time Reconstruction Model From and To Any View at Any Time","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-15T18:46:45.887314Z"},"links":{"citing_paper":"/paper/2506.18890"},"observation_digest":"sha256:611e90e18fd979cc1eb5789933d89e395b32edd4e73a9127fae20c517939d585","observation_id":"ad1bb2a1-2ba1-493d-a130-d9895f5735b3","resolution":{"observed_at":"2026-08-15T18:46:45.887314Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:46:45.892019Z","title":"Accurate, dense, and robust multiview stereopsis.IEEE transactions on pattern analysis and machine intelligence, 32(8):1362–1376, 2009","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2506.18890","last_updated":"2025-06-23T17:57:47Z","snapshot_observed_at":"2026-08-19T17:54:23.387474Z","submitted_at":"2025-06-23T17:57:47Z","title":"4D-LRM: Large Space-Time Reconstruction Model From and To Any View at Any Time","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-15T18:46:45.892019Z"},"links":{"citing_paper":"/paper/2506.18890"},"observation_digest":"sha256:77e32b8923282885480cd98c668c1e71321cbd48fd2f4ada06756ac1d0be305e","observation_id":"755bf913-c4b2-4cda-80a7-76313d82bea4","resolution":{"observed_at":"2026-08-15T18:46:45.892019Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.12365","last_updated":"2024-05-13T19:06:48Z","snapshot_observed_at":"2026-08-16T14:08:33.916046Z","submitted_at":"2024-03-19T02:22:21Z","title":"GaussianFlow: Splatting Gaussian Dynamics for 4D Content Creation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.12365","snapshot_observed_at":"2026-08-15T18:46:45.897300Z","title":"Gaussianflow: Splatting gaussian dynamicsfor4dcontent creation.arXiv preprint arXiv:2403.12365, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.18890","last_updated":"2025-06-23T17:57:47Z","snapshot_observed_at":"2026-08-19T17:54:23.387474Z","submitted_at":"2025-06-23T17:57:47Z","title":"4D-LRM: Large Space-Time Reconstruction Model From and To Any View at Any Time","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-15T18:46:45.897300Z"},"links":{"cited_paper":"/paper/2403.12365","citing_paper":"/paper/2506.18890"},"observation_digest":"sha256:337e79288ad4e67214a85dce341b485b4aa7a2a11e966037fd785b70681c8c0c","observation_id":"efca2e03-b3c8-4f9f-ba39-a62919da4a80","resolution":{"observed_at":"2026-08-15T18:46:45.897300Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:46:45.902890Z","title":"Deep residual learning for image recognition","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2506.18890","last_updated":"2025-06-23T17:57:47Z","snapshot_observed_at":"2026-08-19T17:54:23.387474Z","submitted_at":"2025-06-23T17:57:47Z","title":"4D-LRM: Large Space-Time Reconstruction Model From and To Any View at Any Time","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-15T18:46:45.902890Z"},"links":{"citing_paper":"/paper/2506.18890"},"observation_digest":"sha256:808040c17b8e48ba9be81d26ca1a80547bc86f2f348a21be8b1cf23aa65975f5","observation_id":"b05d0e39-b257-4cd7-89bc-d627cacd7fc2","resolution":{"observed_at":"2026-08-15T18:46:45.902890Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:46:47.863589Z","title":"Lrm: Large reconstruction model for single image to 3d","venue":null,"work_id":"2e8178ae-3eef-405d-8f41-da7a78149db1","year":2024},"citing_paper":{"arxiv_id":"2506.18890","last_updated":"2025-06-23T17:57:47Z","snapshot_observed_at":"2026-08-19T17:54:23.387474Z","submitted_at":"2025-06-23T17:57:47Z","title":"4D-LRM: Large Space-Time Reconstruction Model From and To Any View at Any Time","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-15T18:46:45.907964Z"},"links":{"citing_paper":"/paper/2506.18890"},"observation_digest":"sha256:2c99a3fc4bbac4e41e8812e18655aa92f00f60a592d34f7efa47d32ee8eccf64","observation_id":"b309bdd0-cac8-4fef-985c-4a08617b9d5a","resolution":{"observed_at":"2026-08-15T18:46:47.869429Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:46:47.847148Z","title":"2d gaussian splatting for geometrically accurate radiance fields","venue":null,"work_id":"c9e00c2e-d447-494c-9b72-a873b9307f7e","year":2024},"citing_paper":{"arxiv_id":"2506.18890","last_updated":"2025-06-23T17:57:47Z","snapshot_observed_at":"2026-08-19T17:54:23.387474Z","submitted_at":"2025-06-23T17:57:47Z","title":"4D-LRM: Large Space-Time Reconstruction Model From and To Any View at Any Time","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-15T18:46:45.912920Z"},"links":{"citing_paper":"/paper/2506.18890"},"observation_digest":"sha256:c2762d8f7262978344e7a7a4000d609c7fef9f9a34c2037d6986f60b70f90053","observation_id":"8846298d-5a7f-4299-bae1-39976a2a975f","resolution":{"observed_at":"2026-08-15T18:46:47.852319Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.08479","last_updated":"2024-06-12T17:59:08Z","snapshot_observed_at":"2026-08-16T13:43:37.672805Z","submitted_at":"2024-06-12T17:59:08Z","title":"Real3D: Scaling Up Large Reconstruction Models with Real-World Images","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.08479","snapshot_observed_at":"2026-08-15T18:46:45.917919Z","title":"Real3d: Scaling up large reconstruction models with real-world images.arXiv preprint arXiv:2406.08479, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.18890","last_updated":"2025-06-23T17:57:47Z","snapshot_observed_at":"2026-08-19T17:54:23.387474Z","submitted_at":"2025-06-23T17:57:47Z","title":"4D-LRM: Large Space-Time Reconstruction Model From and To Any View at Any Time","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-15T18:46:45.917919Z"},"links":{"cited_paper":"/paper/2406.08479","citing_paper":"/paper/2506.18890"},"observation_digest":"sha256:2dcc5c8da45b0ca38e53629b9dd12fc600a671ab025f59dbede422a6721718b3","observation_id":"6031207c-193e-4b89-9004-6fc3f0c19e67","resolution":{"observed_at":"2026-08-15T18:46:45.917919Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2505.00702","last_updated":"2025-05-01T17:59:34Z","snapshot_observed_at":"2026-08-16T14:10:24.423487Z","submitted_at":"2025-05-01T17:59:34Z","title":"RayZer: A Self-supervised Large View Synthesis Model","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2505.00702","snapshot_observed_at":"2026-08-15T18:46:45.923317Z","title":"Rayzer: A self-supervised large view synthesis model","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.18890","last_updated":"2025-06-23T17:57:47Z","snapshot_observed_at":"2026-08-19T17:54:23.387474Z","submitted_at":"2025-06-23T17:57:47Z","title":"4D-LRM: Large Space-Time Reconstruction Model From and To Any View at Any Time","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-15T18:46:45.923317Z"},"links":{"cited_paper":"/paper/2505.00702","citing_paper":"/paper/2506.18890"},"observation_digest":"sha256:6233ed3a0b0d1458ce2817e8ff9d87ca3157c44ea164c513322a642333c0c9a9","observation_id":"52cf2272-c801-4059-8cc6-e95862f667ee","resolution":{"observed_at":"2026-08-15T18:46:45.923317Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:46:45.928663Z","title":"Consistent4d: Consistent 360° dynamic object generation from monocular video","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.18890","last_updated":"2025-06-23T17:57:47Z","snapshot_observed_at":"2026-08-19T17:54:23.387474Z","submitted_at":"2025-06-23T17:57:47Z","title":"4D-LRM: Large Space-Time Reconstruction Model From and To Any View at Any Time","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-15T18:46:45.928663Z"},"links":{"citing_paper":"/paper/2506.18890"},"observation_digest":"sha256:b328b92ba06daf66256985e0193f3211109cff73cb222840e0dab55dd45e0364","observation_id":"c1d9cb4b-081e-4c55-8aa0-ed2eb6328250","resolution":{"observed_at":"2026-08-15T18:46:45.928663Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.17242","last_updated":"2025-04-02T21:12:32Z","snapshot_observed_at":"2026-08-16T13:06:53.525883Z","submitted_at":"2024-10-22T17:58:28Z","title":"LVSM: A Large View Synthesis Model with Minimal 3D Inductive Bias","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.17242","snapshot_observed_at":"2026-08-15T18:46:45.932889Z","title":"Lvsm: A large view synthesis model with minimal 3d inductive bias","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.18890","last_updated":"2025-06-23T17:57:47Z","snapshot_observed_at":"2026-08-19T17:54:23.387474Z","submitted_at":"2025-06-23T17:57:47Z","title":"4D-LRM: Large Space-Time Reconstruction Model From and To Any View at Any Time","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-15T18:46:45.932889Z"},"links":{"cited_paper":"/paper/2410.17242","citing_paper":"/paper/2506.18890"},"observation_digest":"sha256:f0192583542233e318abe9a13c6af311cb9c1a0fb25e1246a9f8945c8de2ec18","observation_id":"2e156514-2563-4708-8998-67d39af67ac5","resolution":{"observed_at":"2026-08-15T18:46:45.932889Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:46:47.818220Z","title":"Geonerf: Generalizing nerf with geometry priors","venue":null,"work_id":"6b6983bc-769c-4adc-83bb-7efad661c30f","year":2022},"citing_paper":{"arxiv_id":"2506.18890","last_updated":"2025-06-23T17:57:47Z","snapshot_observed_at":"2026-08-19T17:54:23.387474Z","submitted_at":"2025-06-23T17:57:47Z","title":"4D-LRM: Large Space-Time Reconstruction Model From and To Any View at Any Time","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-15T18:46:45.937667Z"},"links":{"citing_paper":"/paper/2506.18890"},"observation_digest":"sha256:520d9b98d17c601ebea3bf55fb61ec9fec553d784c0a2ab80ec883e6679d7d17","observation_id":"05d94ca5-6820-4ea7-a8a0-b1e07d663e8c","resolution":{"observed_at":"2026-08-15T18:46:47.823923Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:46:45.941992Z","title":"3d gaussian splatting for real-time radiance field rendering.ACM Transactions on Graphics, 42(4):1–14, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.18890","last_updated":"2025-06-23T17:57:47Z","snapshot_observed_at":"2026-08-19T17:54:23.387474Z","submitted_at":"2025-06-23T17:57:47Z","title":"4D-LRM: Large Space-Time Reconstruction Model From and To Any View at Any Time","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-15T18:46:45.941992Z"},"links":{"citing_paper":"/paper/2506.18890"},"observation_digest":"sha256:c8d3ee4cd50a10c8e08479a1be9829812470143a5db7afdc62bf6f961f5d625e","observation_id":"7cddecc9-a347-428d-8a64-9f6921927883","resolution":{"observed_at":"2026-08-15T18:46:45.941992Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:46:47.788761Z","title":"Robotseerobotdo: Imitatingarticulatedobjectmanipulationwithmonocular 4d reconstruction","venue":null,"work_id":"d1917683-306c-462b-9916-a8ce1cff06be","year":2024},"citing_paper":{"arxiv_id":"2506.18890","last_updated":"2025-06-23T17:57:47Z","snapshot_observed_at":"2026-08-19T17:54:23.387474Z","submitted_at":"2025-06-23T17:57:47Z","title":"4D-LRM: Large Space-Time Reconstruction Model From and To Any View at Any Time","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-15T18:46:45.946366Z"},"links":{"citing_paper":"/paper/2506.18890"},"observation_digest":"sha256:f48994d0510dff07460359e55f814dd89d50e8c2c9b02578d39e03dcdee7a49a","observation_id":"3a875d33-9976-4305-adff-485ff99c8329","resolution":{"observed_at":"2026-08-15T18:46:47.794603Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:46:47.772104Z","title":"Dynmf: Neural motion factorization for real-time dynamic view synthesis with 3d gaussian splatting","venue":null,"work_id":"854efa97-1c86-4c4c-8342-b2c67a7ea899","year":2024},"citing_paper":{"arxiv_id":"2506.18890","last_updated":"2025-06-23T17:57:47Z","snapshot_observed_at":"2026-08-19T17:54:23.387474Z","submitted_at":"2025-06-23T17:57:47Z","title":"4D-LRM: Large Space-Time Reconstruction Model From and To Any View at Any Time","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-15T18:46:45.951202Z"},"links":{"citing_paper":"/paper/2506.18890"},"observation_digest":"sha256:289caa9f1ee35386dee00e29d8dccf5ce99013d4dd846796aed714b13d649275","observation_id":"763b3510-3248-4b46-b4b8-146330168672","resolution":{"observed_at":"2026-08-15T18:46:47.777159Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:46:47.755510Z","title":"xformers: A modular and hackable transformer modelling library","venue":null,"work_id":"278027dc-4f63-4ae2-a196-9113df909d29","year":2022},"citing_paper":{"arxiv_id":"2506.18890","last_updated":"2025-06-23T17:57:47Z","snapshot_observed_at":"2026-08-19T17:54:23.387474Z","submitted_at":"2025-06-23T17:57:47Z","title":"4D-LRM: Large Space-Time Reconstruction Model From and To Any View at Any Time","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-15T18:46:45.956042Z"},"links":{"citing_paper":"/paper/2506.18890"},"observation_digest":"sha256:03ff70756e20790f20b534a0e0ad1534877db01bd0a5aac0426b3dc41469556f","observation_id":"ebb36897-4835-4294-9f8c-826bdb560d9c","resolution":{"observed_at":"2026-08-15T18:46:47.760650Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:46:45.961237Z","title":"Grounding image matching in 3d with mast3r","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.18890","last_updated":"2025-06-23T17:57:47Z","snapshot_observed_at":"2026-08-19T17:54:23.387474Z","submitted_at":"2025-06-23T17:57:47Z","title":"4D-LRM: Large Space-Time Reconstruction Model From and To Any View at Any Time","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-15T18:46:45.961237Z"},"links":{"citing_paper":"/paper/2506.18890"},"observation_digest":"sha256:7f4336db9a98b52c409702db91017a8c6baf0a372d18842b4918f2751c2f8248","observation_id":"cca0c874-fda2-48c5-80f6-75d765997468","resolution":{"observed_at":"2026-08-15T18:46:45.961237Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:46:47.727329Z","title":"Vivid-zoo: Multi-view video generation with diffusion model","venue":null,"work_id":"37b20b00-d12d-4cf8-872c-72c9c33e6eca","year":2024},"citing_paper":{"arxiv_id":"2506.18890","last_updated":"2025-06-23T17:57:47Z","snapshot_observed_at":"2026-08-19T17:54:23.387474Z","submitted_at":"2025-06-23T17:57:47Z","title":"4D-LRM: Large Space-Time Reconstruction Model From and To Any View at Any Time","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-15T18:46:45.966414Z"},"links":{"citing_paper":"/paper/2506.18890"},"observation_digest":"sha256:6c142af57324ec5f3a612cf71a22ef6ab11c05841483dded2dd314897308572f","observation_id":"46b8f891-f251-4308-bbb3-e14a57b0febb","resolution":{"observed_at":"2026-08-15T18:46:47.732987Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:46:47.710309Z","title":"Instant3d: Fast text-to-3d with sparse-view generationandlargereconstructionmodel","venue":null,"work_id":"89dcaecc-7aa5-45a8-963e-dfa048d639a4","year":2024},"citing_paper":{"arxiv_id":"2506.18890","last_updated":"2025-06-23T17:57:47Z","snapshot_observed_at":"2026-08-19T17:54:23.387474Z","submitted_at":"2025-06-23T17:57:47Z","title":"4D-LRM: Large Space-Time Reconstruction Model From and To Any View at Any Time","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-15T18:46:45.971399Z"},"links":{"citing_paper":"/paper/2506.18890"},"observation_digest":"sha256:a680888ab02b7c454991fc51b349ddd5def77e7572e2179c00f2ab54bc87a72b","observation_id":"8b290401-14ed-4b40-bc84-9cc65defc64f","resolution":{"observed_at":"2026-08-15T18:46:47.715623Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:46:47.692510Z","title":"Diffusion4d: Fast spatial-temporal consistent 4d generationviavideodiffusionmodels","venue":null,"work_id":"8a5f83ac-d38e-4be8-ac17-b11a48568a12","year":2024},"citing_paper":{"arxiv_id":"2506.18890","last_updated":"2025-06-23T17:57:47Z","snapshot_observed_at":"2026-08-19T17:54:23.387474Z","submitted_at":"2025-06-23T17:57:47Z","title":"4D-LRM: Large Space-Time Reconstruction Model From and To Any View at Any Time","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-15T18:46:45.976278Z"},"links":{"citing_paper":"/paper/2506.18890"},"observation_digest":"sha256:5129f3d162d985e7896c92fb964856779fa8903b718482c26dc849ce2341c856","observation_id":"05f1bd72-bac6-4c28-ba6a-8cdac6acf6ae","resolution":{"observed_at":"2026-08-15T18:46:47.698098Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:46:45.981232Z","title":"Feed-forward bullet-time reconstruction of dynamic scenes from monocular videos.arXiv preprint arXiv:2412.03526, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.18890","last_updated":"2025-06-23T17:57:47Z","snapshot_observed_at":"2026-08-19T17:54:23.387474Z","submitted_at":"2025-06-23T17:57:47Z","title":"4D-LRM: Large Space-Time Reconstruction Model From and To Any View at Any Time","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-15T18:46:45.981232Z"},"links":{"citing_paper":"/paper/2506.18890"},"observation_digest":"sha256:a0715bd8206e988554a0f40630f00ee4bd8a9d84dd5a5254bdb679686777c693","observation_id":"ee2ec805-fc29-485a-9354-75fadcac64cb","resolution":{"observed_at":"2026-08-15T18:46:45.981232Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:46:47.674924Z","title":"Gaufre: Gaussian deformation fields for real-time dynamic novel view synthesis","venue":null,"work_id":"3dfdd25c-af98-4dd0-ae6c-e845519c9d9b","year":2025},"citing_paper":{"arxiv_id":"2506.18890","last_updated":"2025-06-23T17:57:47Z","snapshot_observed_at":"2026-08-19T17:54:23.387474Z","submitted_at":"2025-06-23T17:57:47Z","title":"4D-LRM: Large Space-Time Reconstruction Model From and To Any View at Any Time","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-15T18:46:45.986164Z"},"links":{"citing_paper":"/paper/2506.18890"},"observation_digest":"sha256:eec70b80f9ea6db0a57d65c4263620d1b6f29da27f49cc489db67056d456c8c6","observation_id":"ba134d5c-693c-4664-ac9e-6a00d325639d","resolution":{"observed_at":"2026-08-15T18:46:47.680541Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:46:47.657288Z","title":"Sparseneus: Fast generalizable neural surface reconstruction from sparse views","venue":null,"work_id":"74108774-e21e-42f3-9cce-30802132825b","year":2022},"citing_paper":{"arxiv_id":"2506.18890","last_updated":"2025-06-23T17:57:47Z","snapshot_observed_at":"2026-08-19T17:54:23.387474Z","submitted_at":"2025-06-23T17:57:47Z","title":"4D-LRM: Large Space-Time Reconstruction Model From and To Any View at Any Time","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-15T18:46:45.990948Z"},"links":{"citing_paper":"/paper/2506.18890"},"observation_digest":"sha256:229f66bc7f25f2c7ddb0db6cd409d7790b506bf3806398cb82ac3ad635978f3d","observation_id":"a625db7f-22a2-416d-a609-2092b3be419b","resolution":{"observed_at":"2026-08-15T18:46:47.662549Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:46:47.639725Z","title":"InProceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 17724–17734, 2023","venue":null,"work_id":"0c6f6486-bd7e-43f2-8470-b8acb303b667","year":2023},"citing_paper":{"arxiv_id":"2506.18890","last_updated":"2025-06-23T17:57:47Z","snapshot_observed_at":"2026-08-19T17:54:23.387474Z","submitted_at":"2025-06-23T17:57:47Z","title":"4D-LRM: Large Space-Time Reconstruction Model From and To Any View at Any Time","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-15T18:46:45.996309Z"},"links":{"citing_paper":"/paper/2506.18890"},"observation_digest":"sha256:b89fb8025030d0878551a9a0496375f2c9c2e0a5125e8500968a945e86fd32cf","observation_id":"03b9a9eb-7bd4-45aa-8a33-3d427fcb7728","resolution":{"observed_at":"2026-08-15T18:46:47.645352Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:46:47.622994Z","title":"Mixed precision training","venue":null,"work_id":"96d5ec9c-64cd-47d5-b43d-54c8433f3c68","year":2018},"citing_paper":{"arxiv_id":"2506.18890","last_updated":"2025-06-23T17:57:47Z","snapshot_observed_at":"2026-08-19T17:54:23.387474Z","submitted_at":"2025-06-23T17:57:47Z","title":"4D-LRM: Large Space-Time Reconstruction Model From and To Any View at Any Time","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-15T18:46:46.001875Z"},"links":{"citing_paper":"/paper/2506.18890"},"observation_digest":"sha256:da3e031977a81dac0ad04c0608a5eacc3e3eb3c41c7a865a7672edc169dadb76","observation_id":"0107cc08-7775-4caf-935e-42dec3ad2ad7","resolution":{"observed_at":"2026-08-15T18:46:47.628052Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:46:47.605245Z","title":"Srinivasan, Matthew Tancik, Jonathan T","venue":null,"work_id":"dbb27a7e-fd18-444f-91d6-941c8b60305f","year":2020},"citing_paper":{"arxiv_id":"2506.18890","last_updated":"2025-06-23T17:57:47Z","snapshot_observed_at":"2026-08-19T17:54:23.387474Z","submitted_at":"2025-06-23T17:57:47Z","title":"4D-LRM: Large Space-Time Reconstruction Model From and To Any View at Any Time","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-15T18:46:46.006811Z"},"links":{"citing_paper":"/paper/2506.18890"},"observation_digest":"sha256:5481df78edafbde969d54d22dcbcdb6278ab6426f89fce9a833cb13dc05bf640","observation_id":"ea905ab4-6c6f-4abd-b2b6-b3992eb3a88f","resolution":{"observed_at":"2026-08-15T18:46:47.611248Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:46:46.011440Z","title":"Efficient4d: Fastdynamic3dobjectgeneration from a single-view video.arXiv preprint arXiv 2401.08742, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.18890","last_updated":"2025-06-23T17:57:47Z","snapshot_observed_at":"2026-08-19T17:54:23.387474Z","submitted_at":"2025-06-23T17:57:47Z","title":"4D-LRM: Large Space-Time Reconstruction Model From and To Any View at Any Time","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-15T18:46:46.011440Z"},"links":{"citing_paper":"/paper/2506.18890"},"observation_digest":"sha256:115012d4b28f8e760c87b03a598817c42d1caa2681e4aaefb0d05f6e9cb3a646","observation_id":"b0ca576e-5918-4999-afcc-cc191561f023","resolution":{"observed_at":"2026-08-15T18:46:46.011440Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:46:47.588141Z","title":"Deepsdf: Learningcontinuoussigneddistancefunctionsforshaperepresentation","venue":null,"work_id":"f570c92d-2d9f-4820-9d45-c152048ce2e2","year":2019},"citing_paper":{"arxiv_id":"2506.18890","last_updated":"2025-06-23T17:57:47Z","snapshot_observed_at":"2026-08-19T17:54:23.387474Z","submitted_at":"2025-06-23T17:57:47Z","title":"4D-LRM: Large Space-Time Reconstruction Model From and To Any View at Any Time","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-15T18:46:46.016199Z"},"links":{"citing_paper":"/paper/2506.18890"},"observation_digest":"sha256:15757547f618783ff680e5661110f1058e966bafb19f128b0452a336688b497b","observation_id":"7fa645bb-6504-4e36-bdd6-aa1345439318","resolution":{"observed_at":"2026-08-15T18:46:47.593237Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:46:46.021061Z","title":"Nerfies: Deformable neural radiance fields","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.18890","last_updated":"2025-06-23T17:57:47Z","snapshot_observed_at":"2026-08-19T17:54:23.387474Z","submitted_at":"2025-06-23T17:57:47Z","title":"4D-LRM: Large Space-Time Reconstruction Model From and To Any View at Any Time","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-15T18:46:46.021061Z"},"links":{"citing_paper":"/paper/2506.18890"},"observation_digest":"sha256:2aff98694770196424757c2a12768f56064e78516947ab70e9c74175d8b74403","observation_id":"dbb97b43-a37a-409e-9063-fb2f296a1196","resolution":{"observed_at":"2026-08-15T18:46:46.021061Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:46:47.559775Z","title":null,"venue":null,"work_id":"49d5323f-2fcb-493e-8260-c8e83c2ec5b9","year":null},"citing_paper":{"arxiv_id":"2506.18890","last_updated":"2025-06-23T17:57:47Z","snapshot_observed_at":"2026-08-19T17:54:23.387474Z","submitted_at":"2025-06-23T17:57:47Z","title":"4D-LRM: Large Space-Time Reconstruction Model From and To Any View at Any Time","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-15T18:46:46.026148Z"},"links":{"citing_paper":"/paper/2506.18890"},"observation_digest":"sha256:823aeaa737698b036f04e722c3cdf3de4913744ebc06248b223526b2b9e4fe50","observation_id":"3dd0ea5b-6b7b-445e-9ec8-4d45abc8dc81","resolution":{"observed_at":"2026-08-15T18:46:47.564833Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:46:47.543514Z","title":"Visual modeling with a hand-held camera.International Journal of Computer Vision, 59:207–232, 2004","venue":null,"work_id":"f8eb7bdc-7f39-4c3a-bfe6-eeb1c8522676","year":2004},"citing_paper":{"arxiv_id":"2506.18890","last_updated":"2025-06-23T17:57:47Z","snapshot_observed_at":"2026-08-19T17:54:23.387474Z","submitted_at":"2025-06-23T17:57:47Z","title":"4D-LRM: Large Space-Time Reconstruction Model From and To Any View at Any Time","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-15T18:46:46.031340Z"},"links":{"citing_paper":"/paper/2506.18890"},"observation_digest":"sha256:b9caaf36f758ace0af782c600bcfb8414cdf7bc7c91536c82cbe27b7ae970607","observation_id":"0723195e-03c3-4e1d-87ce-7f1df40c894b","resolution":{"observed_at":"2026-08-15T18:46:47.548556Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:46:47.526589Z","title":"Detailed real-time urban 3d reconstruction from video.International Journal of Computer Vision, 78:143–167, 2008","venue":null,"work_id":"23ee1bdc-56d3-4271-921c-d98e4845ccdc","year":2008},"citing_paper":{"arxiv_id":"2506.18890","last_updated":"2025-06-23T17:57:47Z","snapshot_observed_at":"2026-08-19T17:54:23.387474Z","submitted_at":"2025-06-23T17:57:47Z","title":"4D-LRM: Large Space-Time Reconstruction Model From and To Any View at Any Time","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-15T18:46:46.036336Z"},"links":{"citing_paper":"/paper/2506.18890"},"observation_digest":"sha256:19781c2dfe721f03c9f9ac08686756e313a9dfd07823e679752405e12ec6a7d0","observation_id":"f24d6080-62f8-4267-95aa-1af484400bc6","resolution":{"observed_at":"2026-08-15T18:46:47.532304Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:46:46.041426Z","title":"D-nerf: Neural radiance fields for dynamic scenes","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.18890","last_updated":"2025-06-23T17:57:47Z","snapshot_observed_at":"2026-08-19T17:54:23.387474Z","submitted_at":"2025-06-23T17:57:47Z","title":"4D-LRM: Large Space-Time Reconstruction Model From and To Any View at Any Time","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-15T18:46:46.041426Z"},"links":{"citing_paper":"/paper/2506.18890"},"observation_digest":"sha256:6cd21638282cd1c4788895fa05fd8c3ec4b29e5857df9b05e53aeffe9efceff2","observation_id":"c19f7adf-c7f3-4830-9b57-9ac88c2620a9","resolution":{"observed_at":"2026-08-15T18:46:46.041426Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2312.17142","last_updated":"2024-06-10T14:07:53Z","snapshot_observed_at":"2026-08-17T13:19:55.931617Z","submitted_at":"2023-12-28T17:16:44Z","title":"DreamGaussian4D: Generative 4D Gaussian Splatting","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2312.17142","snapshot_observed_at":"2026-08-15T18:46:46.046307Z","title":"Dreamgaussian4d: Generative 4d gaussian splatting.arXiv preprint arXiv:2312.17142, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.18890","last_updated":"2025-06-23T17:57:47Z","snapshot_observed_at":"2026-08-19T17:54:23.387474Z","submitted_at":"2025-06-23T17:57:47Z","title":"4D-LRM: Large Space-Time Reconstruction Model From and To Any View at Any Time","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-15T18:46:46.046307Z"},"links":{"cited_paper":"/paper/2312.17142","citing_paper":"/paper/2506.18890"},"observation_digest":"sha256:243d9acebf9b51e3198552f699c079cb132ef3336c423039621356996ac78fe7","observation_id":"62cc212a-f3ef-464d-8bbe-fbfed3ce9a1a","resolution":{"observed_at":"2026-08-15T18:46:46.046307Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:46:47.499770Z","title":"L4gm: Large 4d gaussian reconstruction model","venue":null,"work_id":"f1c70300-4720-4d33-9fc7-a9d0252f71ba","year":2024},"citing_paper":{"arxiv_id":"2506.18890","last_updated":"2025-06-23T17:57:47Z","snapshot_observed_at":"2026-08-19T17:54:23.387474Z","submitted_at":"2025-06-23T17:57:47Z","title":"4D-LRM: Large Space-Time Reconstruction Model From and To Any View at Any Time","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-15T18:46:46.051326Z"},"links":{"citing_paper":"/paper/2506.18890"},"observation_digest":"sha256:3c4ddcf4484301a49f37930faca8a0d6ac320a08412b8609b676fe9f0de2ce9d","observation_id":"357ec880-0774-47b2-9eea-eeb626551d95","resolution":{"observed_at":"2026-08-15T18:46:47.504802Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:46:46.057750Z","title":"Structure-from-motion revisited","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2506.18890","last_updated":"2025-06-23T17:57:47Z","snapshot_observed_at":"2026-08-19T17:54:23.387474Z","submitted_at":"2025-06-23T17:57:47Z","title":"4D-LRM: Large Space-Time Reconstruction Model From and To Any View at Any Time","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-15T18:46:46.057750Z"},"links":{"citing_paper":"/paper/2506.18890"},"observation_digest":"sha256:eef3b9373df3526013a712de91187212276ebc4fb2c657c92f62138ee360975d","observation_id":"f345c784-b63a-486c-85d0-aef8091b133f","resolution":{"observed_at":"2026-08-15T18:46:46.057750Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:46:47.471748Z","title":"Pixelwise view selection for unstructured multi-view stereo","venue":null,"work_id":"124ab77d-4329-4874-a84a-ed659477e6db","year":2016},"citing_paper":{"arxiv_id":"2506.18890","last_updated":"2025-06-23T17:57:47Z","snapshot_observed_at":"2026-08-19T17:54:23.387474Z","submitted_at":"2025-06-23T17:57:47Z","title":"4D-LRM: Large Space-Time Reconstruction Model From and To Any View at Any Time","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-15T18:46:46.062782Z"},"links":{"citing_paper":"/paper/2506.18890"},"observation_digest":"sha256:5c66b12818ad4a94974a55c38bb4881df2c8f4dd668c69d856353984a1291c42","observation_id":"d5bfeddb-a126-46e5-ac32-fa26b1ed625a","resolution":{"observed_at":"2026-08-15T18:46:47.476722Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:46:47.455422Z","title":null,"venue":null,"work_id":"b1d01a2e-05b1-48a4-b227-913a29816f12","year":2024},"citing_paper":{"arxiv_id":"2506.18890","last_updated":"2025-06-23T17:57:47Z","snapshot_observed_at":"2026-08-19T17:54:23.387474Z","submitted_at":"2025-06-23T17:57:47Z","title":"4D-LRM: Large Space-Time Reconstruction Model From and To Any View at Any Time","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-15T18:46:46.067749Z"},"links":{"citing_paper":"/paper/2506.18890"},"observation_digest":"sha256:0ef77030829d793873045e5aa03b25f0f0d0801c87dd0d51f8df3bfdeb939215","observation_id":"1d82e5dd-ab94-4553-b2c0-c760c94a92d7","resolution":{"observed_at":"2026-08-15T18:46:47.460437Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:46:47.439382Z","title":"Phototourism: exploringphotocollections in 3d.ACM Transactions on Graphics (TOG), 25(3):835–846, 2006","venue":null,"work_id":"d6251f1d-7b42-4169-ae1b-ce092db43b7f","year":2006},"citing_paper":{"arxiv_id":"2506.18890","last_updated":"2025-06-23T17:57:47Z","snapshot_observed_at":"2026-08-19T17:54:23.387474Z","submitted_at":"2025-06-23T17:57:47Z","title":"4D-LRM: Large Space-Time Reconstruction Model From and To Any View at Any Time","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-15T18:46:46.072599Z"},"links":{"citing_paper":"/paper/2506.18890"},"observation_digest":"sha256:65ee268a2c746c74cd3fa4431803bbddab2f5e8af31d0473e6f452ba2f492a58","observation_id":"c2930141-bce1-4c29-abc7-92d79487957a","resolution":{"observed_at":"2026-08-15T18:46:47.444319Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:46:47.423211Z","title":"Generalizable patch-based neural rendering","venue":null,"work_id":"6db3e25d-cc12-4a7b-a6cf-34bb6b9ba653","year":2022},"citing_paper":{"arxiv_id":"2506.18890","last_updated":"2025-06-23T17:57:47Z","snapshot_observed_at":"2026-08-19T17:54:23.387474Z","submitted_at":"2025-06-23T17:57:47Z","title":"4D-LRM: Large Space-Time Reconstruction Model From and To Any View at Any Time","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-15T18:46:46.077783Z"},"links":{"citing_paper":"/paper/2506.18890"},"observation_digest":"sha256:2ee661472f0786472cfb55a6dc35a199e39a4192fe46281b7b77079a146b270e","observation_id":"96e51965-906d-475e-a32a-a4ffd94a40b2","resolution":{"observed_at":"2026-08-15T18:46:47.428180Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:46:47.407721Z","title":"Light field neural rendering","venue":null,"work_id":"fab3ea0c-aaa0-4356-8dd4-57e01e77bf82","year":2022},"citing_paper":{"arxiv_id":"2506.18890","last_updated":"2025-06-23T17:57:47Z","snapshot_observed_at":"2026-08-19T17:54:23.387474Z","submitted_at":"2025-06-23T17:57:47Z","title":"4D-LRM: Large Space-Time Reconstruction Model From and To Any View at Any Time","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-15T18:46:46.083406Z"},"links":{"citing_paper":"/paper/2506.18890"},"observation_digest":"sha256:57517bbc2a3889b8798f4b78a67be8b44e48ee72c79fb78952a0263a9e68aeee","observation_id":"21504ea4-3991-4c33-a528-eac9af13b1ad","resolution":{"observed_at":"2026-08-15T18:46:47.412763Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:46:47.390855Z","title":"Lgm: Large multi-view gaussian model for high-resolution 3d content creation","venue":null,"work_id":"f460008a-e482-42a2-97eb-0df0d91d0264","year":2024},"citing_paper":{"arxiv_id":"2506.18890","last_updated":"2025-06-23T17:57:47Z","snapshot_observed_at":"2026-08-19T17:54:23.387474Z","submitted_at":"2025-06-23T17:57:47Z","title":"4D-LRM: Large Space-Time Reconstruction Model From and To Any View at Any Time","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-15T18:46:46.088597Z"},"links":{"citing_paper":"/paper/2506.18890"},"observation_digest":"sha256:4fbe71caf04d9262bd67dd3ecdd7fecb0d92b80303eff4452902ac9faadf676f","observation_id":"215cd69b-6d00-4106-91dc-b07230a540e8","resolution":{"observed_at":"2026-08-15T18:46:47.396051Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:46:47.373902Z","title":"Mv-dust3r+: Single-stage scene reconstruction from sparse views in 2 seconds","venue":null,"work_id":"3a169a69-d7fb-405b-94dd-43bf8c6a7c81","year":2024},"citing_paper":{"arxiv_id":"2506.18890","last_updated":"2025-06-23T17:57:47Z","snapshot_observed_at":"2026-08-19T17:54:23.387474Z","submitted_at":"2025-06-23T17:57:47Z","title":"4D-LRM: Large Space-Time Reconstruction Model From and To Any View at Any Time","version":1},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-15T18:46:46.093727Z"},"links":{"citing_paper":"/paper/2506.18890"},"observation_digest":"sha256:4420c4f7fe0c2d0dcf9d3177e829faaaaae09d4f8e3243a469713777bd2e766b","observation_id":"b182527d-9709-416e-a51e-a69300b24f7e","resolution":{"observed_at":"2026-08-15T18:46:47.379143Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:46:47.355867Z","title":"Mononerf: Learning a generalizable dynamic radiancefieldfrommonocularvideos","venue":null,"work_id":"799b94c2-bab9-488e-b82a-67ef24683005","year":2023},"citing_paper":{"arxiv_id":"2506.18890","last_updated":"2025-06-23T17:57:47Z","snapshot_observed_at":"2026-08-19T17:54:23.387474Z","submitted_at":"2025-06-23T17:57:47Z","title":"4D-LRM: Large Space-Time Reconstruction Model From and To Any View at Any Time","version":1},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-15T18:46:46.098765Z"},"links":{"citing_paper":"/paper/2506.18890"},"observation_digest":"sha256:f66cb83eb3ba07a676adf1979e0e5e853dc4f124d537b60ac0e2da98b180ba27","observation_id":"ea176186-b0dd-48ab-a576-6c6e766b8073","resolution":{"observed_at":"2026-08-15T18:46:47.362154Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:46:47.338333Z","title":"Non-rigid neural radiance fields: Reconstruction and novel view synthesis of a dynamic scene from monocular video","venue":null,"work_id":"9cf370ac-f15a-4db6-a0dc-cdd5acc9794e","year":2021},"citing_paper":{"arxiv_id":"2506.18890","last_updated":"2025-06-23T17:57:47Z","snapshot_observed_at":"2026-08-19T17:54:23.387474Z","submitted_at":"2025-06-23T17:57:47Z","title":"4D-LRM: Large Space-Time Reconstruction Model From and To Any View at Any Time","version":1},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-15T18:46:46.103169Z"},"links":{"citing_paper":"/paper/2506.18890"},"observation_digest":"sha256:62d85af651a48aadd871ca9fc50e64e9b6a33b66fee1003ea945928c49494b77","observation_id":"c57a507c-845b-48f0-8032-75ad03150df7","resolution":{"observed_at":"2026-08-15T18:46:47.343808Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:46:47.319520Z","title":"Attention is all you need","venue":null,"work_id":"850964f0-9b7e-440d-b6ce-3b337b8cb692","year":2017},"citing_paper":{"arxiv_id":"2506.18890","last_updated":"2025-06-23T17:57:47Z","snapshot_observed_at":"2026-08-19T17:54:23.387474Z","submitted_at":"2025-06-23T17:57:47Z","title":"4D-LRM: Large Space-Time Reconstruction Model From and To Any View at Any Time","version":1},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-15T18:46:46.107745Z"},"links":{"citing_paper":"/paper/2506.18890"},"observation_digest":"sha256:d1ebab14f96450c3b956c4e2d149f890b2bc3b2e3145b56a51b28b08705fb27b","observation_id":"1c227b22-429f-49f4-b41e-7ee62356223a","resolution":{"observed_at":"2026-08-15T18:46:47.325593Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:46:47.301496Z","title":"Sv3d: Novel multi-view synthesis and 3d generation from a single image using latent video diffusion","venue":null,"work_id":"9e05ed47-76e7-4820-80b9-adaa25ca5171","year":2024},"citing_paper":{"arxiv_id":"2506.18890","last_updated":"2025-06-23T17:57:47Z","snapshot_observed_at":"2026-08-19T17:54:23.387474Z","submitted_at":"2025-06-23T17:57:47Z","title":"4D-LRM: Large Space-Time Reconstruction Model From and To Any View at Any Time","version":1},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-15T18:46:46.112559Z"},"links":{"citing_paper":"/paper/2506.18890"},"observation_digest":"sha256:c87007f58eb6350a98df87e8ab7abad8499e33cf551c23fead75a83327b5909a","observation_id":"1a256947-6f9f-432a-8f66-8351f4685c99","resolution":{"observed_at":"2026-08-15T18:46:47.306506Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:46:47.284645Z","title":"Vggt: Visual geometry grounded transformer","venue":null,"work_id":"9c3a1577-d68f-432c-852b-a445d8f26e30","year":2025},"citing_paper":{"arxiv_id":"2506.18890","last_updated":"2025-06-23T17:57:47Z","snapshot_observed_at":"2026-08-19T17:54:23.387474Z","submitted_at":"2025-06-23T17:57:47Z","title":"4D-LRM: Large Space-Time Reconstruction Model From and To Any View at Any Time","version":1},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-08-15T18:46:46.116865Z"},"links":{"citing_paper":"/paper/2506.18890"},"observation_digest":"sha256:1e175e72d0c3e77a557471739c5c2921cf7f18145033c60a873deb2424c30dc7","observation_id":"8b98b2cb-f56d-4d84-9f39-56e45c6b265d","resolution":{"observed_at":"2026-08-15T18:46:47.289923Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:46:47.266942Z","title":"Pf-lrm: Pose-free large reconstruction model for joint pose and shape prediction","venue":null,"work_id":"b3df9984-f79e-4f9d-84c6-76ea849bd59c","year":2024},"citing_paper":{"arxiv_id":"2506.18890","last_updated":"2025-06-23T17:57:47Z","snapshot_observed_at":"2026-08-19T17:54:23.387474Z","submitted_at":"2025-06-23T17:57:47Z","title":"4D-LRM: Large Space-Time Reconstruction Model From and To Any View at Any Time","version":1},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-08-15T18:46:46.122025Z"},"links":{"citing_paper":"/paper/2506.18890"},"observation_digest":"sha256:7b7f0d4a15c3021233f7af8d101aef4ec76aaa8d8d9aad36bd1418ef48004f07","observation_id":"dfd48367-dd0c-4632-9c08-9dedc968a75c","resolution":{"observed_at":"2026-08-15T18:46:47.272797Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:46:47.249184Z","title":"Ibrnet: Learning multi-view image-based rendering","venue":null,"work_id":"a9084b83-0153-4493-ae01-17ee4fe304ac","year":2021},"citing_paper":{"arxiv_id":"2506.18890","last_updated":"2025-06-23T17:57:47Z","snapshot_observed_at":"2026-08-19T17:54:23.387474Z","submitted_at":"2025-06-23T17:57:47Z","title":"4D-LRM: Large Space-Time Reconstruction Model From and To Any View at Any Time","version":1},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-08-15T18:46:46.126472Z"},"links":{"citing_paper":"/paper/2506.18890"},"observation_digest":"sha256:edfce911299a72d3f7a3c316480bec513e4239c05e1f173f1dcc44318e534774","observation_id":"4dfef906-e353-479a-ad79-e7310f31cb89","resolution":{"observed_at":"2026-08-15T18:46:47.254781Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:46:46.130790Z","title":"Shape of motion: 4d reconstruction from a single video.arXiv preprint arXiv:2407.13764, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.18890","last_updated":"2025-06-23T17:57:47Z","snapshot_observed_at":"2026-08-19T17:54:23.387474Z","submitted_at":"2025-06-23T17:57:47Z","title":"4D-LRM: Large Space-Time Reconstruction Model From and To Any View at Any Time","version":1},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-08-15T18:46:46.130790Z"},"links":{"citing_paper":"/paper/2506.18890"},"observation_digest":"sha256:b94173d9d295b772d95371e4413ce1d5b42b4da32ffb4ae86b378c7bb76afc29","observation_id":"96735bca-254d-4537-b66a-0eb722cb3f71","resolution":{"observed_at":"2026-08-15T18:46:46.130790Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:46:47.229405Z","title":"Continuous 3d perception model with persistent state","venue":null,"work_id":"63a6418c-8a2e-4161-bbc9-bd94aaa672d7","year":2025},"citing_paper":{"arxiv_id":"2506.18890","last_updated":"2025-06-23T17:57:47Z","snapshot_observed_at":"2026-08-19T17:54:23.387474Z","submitted_at":"2025-06-23T17:57:47Z","title":"4D-LRM: Large Space-Time Reconstruction Model From and To Any View at Any Time","version":1},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-08-15T18:46:46.135920Z"},"links":{"citing_paper":"/paper/2506.18890"},"observation_digest":"sha256:947ae915e0cb7f52596d11e46417a082a3dbfcc6c8ab1008c761b470fb6b4a18","observation_id":"2c551e92-ff93-4532-bf1c-5801b510ec27","resolution":{"observed_at":"2026-08-15T18:46:47.237127Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:46:46.140960Z","title":"Dust3r: Geometric 3d vision made easy","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.18890","last_updated":"2025-06-23T17:57:47Z","snapshot_observed_at":"2026-08-19T17:54:23.387474Z","submitted_at":"2025-06-23T17:57:47Z","title":"4D-LRM: Large Space-Time Reconstruction Model From and To Any View at Any Time","version":1},"reference_index":71,"source":"pdf_text","source_observed_at":"2026-08-15T18:46:46.140960Z"},"links":{"citing_paper":"/paper/2506.18890"},"observation_digest":"sha256:f7113f1501c8a9e6fb5043e4962994a519d6a30447e967c05a03692f46dc3e0b","observation_id":"9d5ccbe2-52ce-4aeb-bbcc-a9856a16a93a","resolution":{"observed_at":"2026-08-15T18:46:46.140960Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:46:46.146379Z","title":"Image quality assessment: from error visibility to structural similarity.IEEE transactions on image processing, 13(4): 600–612, 2004","venue":null,"work_id":null,"year":2004},"citing_paper":{"arxiv_id":"2506.18890","last_updated":"2025-06-23T17:57:47Z","snapshot_observed_at":"2026-08-19T17:54:23.387474Z","submitted_at":"2025-06-23T17:57:47Z","title":"4D-LRM: Large Space-Time Reconstruction Model From and To Any View at Any Time","version":1},"reference_index":72,"source":"pdf_text","source_observed_at":"2026-08-15T18:46:46.146379Z"},"links":{"citing_paper":"/paper/2506.18890"},"observation_digest":"sha256:4c0c428f1ecbb1305902ea4c8ee5e60160318a49915856768e489d7eda4df857","observation_id":"6857d84e-9c56-4e72-9306-eeb1d71c1d9a","resolution":{"observed_at":"2026-08-15T18:46:46.146379Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:46:47.185047Z","title":"Controlling space and time with diffusion models","venue":null,"work_id":"15bac215-d362-4f32-9b1d-0ab0fb8b392d","year":2024},"citing_paper":{"arxiv_id":"2506.18890","last_updated":"2025-06-23T17:57:47Z","snapshot_observed_at":"2026-08-19T17:54:23.387474Z","submitted_at":"2025-06-23T17:57:47Z","title":"4D-LRM: Large Space-Time Reconstruction Model From and To Any View at Any Time","version":1},"reference_index":73,"source":"pdf_text","source_observed_at":"2026-08-15T18:46:46.151789Z"},"links":{"citing_paper":"/paper/2506.18890"},"observation_digest":"sha256:2ec4160c5252f6374371f1282badb756915656005f5f30f4c56a22de65e8f081","observation_id":"e9b10987-867e-4181-85c2-61fdd74d57eb","resolution":{"observed_at":"2026-08-15T18:46:47.190482Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2404.12385","last_updated":"2025-01-23T01:55:24Z","snapshot_observed_at":"2026-08-16T13:59:40.318424Z","submitted_at":"2024-04-18T17:59:41Z","title":"MeshLRM: Large Reconstruction Model for High-Quality Meshes","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.12385","snapshot_observed_at":"2026-08-15T18:46:46.156993Z","title":"Meshlrm: Large reconstruction model for high-quality mesh.arXiv preprint arXiv:2404.12385, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.18890","last_updated":"2025-06-23T17:57:47Z","snapshot_observed_at":"2026-08-19T17:54:23.387474Z","submitted_at":"2025-06-23T17:57:47Z","title":"4D-LRM: Large Space-Time Reconstruction Model From and To Any View at Any Time","version":1},"reference_index":74,"source":"pdf_text","source_observed_at":"2026-08-15T18:46:46.156993Z"},"links":{"cited_paper":"/paper/2404.12385","citing_paper":"/paper/2506.18890"},"observation_digest":"sha256:7825dbf809b7b0c93fc04507a23ac31d1b201add1b815e3573a4f3f068c1cc54","observation_id":"0f521d0e-891a-4567-b5d4-f26e97fd057e","resolution":{"observed_at":"2026-08-15T18:46:46.156993Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:46:46.163159Z","title":"4d gaussian splatting for real-time dynamic scene rendering","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.18890","last_updated":"2025-06-23T17:57:47Z","snapshot_observed_at":"2026-08-19T17:54:23.387474Z","submitted_at":"2025-06-23T17:57:47Z","title":"4D-LRM: Large Space-Time Reconstruction Model From and To Any View at Any Time","version":1},"reference_index":75,"source":"pdf_text","source_observed_at":"2026-08-15T18:46:46.163159Z"},"links":{"citing_paper":"/paper/2506.18890"},"observation_digest":"sha256:f0b7ec670331d440d1fc8e4966e13344263e8d86010da4d345982fc78a1458e9","observation_id":"b0e28593-fe61-4e95-8bf8-c12892124d95","resolution":{"observed_at":"2026-08-15T18:46:46.163159Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2411.18613","last_updated":"2024-12-18T21:21:07Z","snapshot_observed_at":"2026-08-17T09:55:47.957559Z","submitted_at":"2024-11-27T18:57:16Z","title":"CAT4D: Create Anything in 4D with Multi-View Video Diffusion Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2411.18613","snapshot_observed_at":"2026-08-15T18:46:46.168232Z","title":"Cat4d: Create anything in 4d with multi-view video diffusion models","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.18890","last_updated":"2025-06-23T17:57:47Z","snapshot_observed_at":"2026-08-19T17:54:23.387474Z","submitted_at":"2025-06-23T17:57:47Z","title":"4D-LRM: Large Space-Time Reconstruction Model From and To Any View at Any Time","version":1},"reference_index":76,"source":"pdf_text","source_observed_at":"2026-08-15T18:46:46.168232Z"},"links":{"cited_paper":"/paper/2411.18613","citing_paper":"/paper/2506.18890"},"observation_digest":"sha256:46ee97aac6c14a6929714b0c0d4134834cb8aa546f1eb9b854cc82e1ccafbada","observation_id":"5a5fa93c-bd72-41c0-a5ad-68f430f68bc7","resolution":{"observed_at":"2026-08-15T18:46:46.168232Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:46:47.155464Z","title":"Lrm-zero: Training large reconstruction models with synthesized data","venue":null,"work_id":"cd075096-cfcc-4570-8683-8a8aa2bc473e","year":2024},"citing_paper":{"arxiv_id":"2506.18890","last_updated":"2025-06-23T17:57:47Z","snapshot_observed_at":"2026-08-19T17:54:23.387474Z","submitted_at":"2025-06-23T17:57:47Z","title":"4D-LRM: Large Space-Time Reconstruction Model From and To Any View at Any Time","version":1},"reference_index":77,"source":"pdf_text","source_observed_at":"2026-08-15T18:46:46.173441Z"},"links":{"citing_paper":"/paper/2506.18890"},"observation_digest":"sha256:8d50b7628f4bfe816a899c6eaf68ab554e6c82bcc301d53c470e12766031f503","observation_id":"eb602a69-6911-4213-8add-d2088d313c5d","resolution":{"observed_at":"2026-08-15T18:46:47.161845Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2407.17470","last_updated":"2025-02-27T21:52:39Z","snapshot_observed_at":"2026-08-18T18:18:22.721632Z","submitted_at":"2024-07-24T17:59:43Z","title":"SV4D: Dynamic 3D Content Generation with Multi-Frame and Multi-View Consistency","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.17470","snapshot_observed_at":"2026-08-15T18:46:46.178097Z","title":"Sv4d: Dy- namic 3d content generation with multi-frame and multi-view consistency.arXiv preprint arXiv:2407.17470, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.18890","last_updated":"2025-06-23T17:57:47Z","snapshot_observed_at":"2026-08-19T17:54:23.387474Z","submitted_at":"2025-06-23T17:57:47Z","title":"4D-LRM: Large Space-Time Reconstruction Model From and To Any View at Any Time","version":1},"reference_index":78,"source":"pdf_text","source_observed_at":"2026-08-15T18:46:46.178097Z"},"links":{"cited_paper":"/paper/2407.17470","citing_paper":"/paper/2506.18890"},"observation_digest":"sha256:734f08833c9de41f2b3b77dc53c0e250ab6f1571e087a6153b27a6761d0ed4fe","observation_id":"d118a411-0f22-482f-9599-8a02309048f2","resolution":{"observed_at":"2026-08-15T18:46:46.178097Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:46:47.135026Z","title":"Dmv3d: Denoising multi-view diffusion using 3d large reconstruction model","venue":null,"work_id":"c1be5774-6b78-4c48-9f59-852e7393d8c6","year":2024},"citing_paper":{"arxiv_id":"2506.18890","last_updated":"2025-06-23T17:57:47Z","snapshot_observed_at":"2026-08-19T17:54:23.387474Z","submitted_at":"2025-06-23T17:57:47Z","title":"4D-LRM: Large Space-Time Reconstruction Model From and To Any View at Any Time","version":1},"reference_index":79,"source":"pdf_text","source_observed_at":"2026-08-15T18:46:46.183484Z"},"links":{"citing_paper":"/paper/2506.18890"},"observation_digest":"sha256:004d218a0a1a058c1c7f97ab59fc911ad625ef48d1292eb6009cce7e81096139","observation_id":"17953bf1-b25f-4916-9261-d71cd22e96b8","resolution":{"observed_at":"2026-08-15T18:46:47.140887Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:46:47.116103Z","title":"Fast3r: Towards 3d reconstruction of 1000+ images in one forward pass","venue":null,"work_id":"da200194-8f83-486b-a974-e5ca882f0de9","year":2025},"citing_paper":{"arxiv_id":"2506.18890","last_updated":"2025-06-23T17:57:47Z","snapshot_observed_at":"2026-08-19T17:54:23.387474Z","submitted_at":"2025-06-23T17:57:47Z","title":"4D-LRM: Large Space-Time Reconstruction Model From and To Any View at Any Time","version":1},"reference_index":80,"source":"pdf_text","source_observed_at":"2026-08-15T18:46:46.188320Z"},"links":{"citing_paper":"/paper/2506.18890"},"observation_digest":"sha256:104b024c7f956c50a1493037a2855339153e46a4dbe390ef1d5c2a17ace9f4d9","observation_id":"34953b7b-d216-4d90-ba47-23b59ac0e28e","resolution":{"observed_at":"2026-08-15T18:46:47.122126Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:46:47.098448Z","title":"Storm: Spatio-temporal reconstruction model for large-scale outdoor scenes","venue":null,"work_id":"ace759d7-2ac0-4265-bea1-84777b34da30","year":2025},"citing_paper":{"arxiv_id":"2506.18890","last_updated":"2025-06-23T17:57:47Z","snapshot_observed_at":"2026-08-19T17:54:23.387474Z","submitted_at":"2025-06-23T17:57:47Z","title":"4D-LRM: Large Space-Time Reconstruction Model From and To Any View at Any Time","version":1},"reference_index":81,"source":"pdf_text","source_observed_at":"2026-08-15T18:46:46.193526Z"},"links":{"citing_paper":"/paper/2506.18890"},"observation_digest":"sha256:ea731861e985074f0bb85aa1e3b6965948ca4e9169cb61db452d041c3c74f91a","observation_id":"d04b6b78-fc8a-4454-906b-5fa82b222eef","resolution":{"observed_at":"2026-08-15T18:46:47.104390Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:46:47.080373Z","title":"Real-time photorealistic dynamic scene representationandrenderingwith4dgaussiansplatting","venue":null,"work_id":"3e7508af-767a-4f9a-8f27-824812ccba1c","year":2024},"citing_paper":{"arxiv_id":"2506.18890","last_updated":"2025-06-23T17:57:47Z","snapshot_observed_at":"2026-08-19T17:54:23.387474Z","submitted_at":"2025-06-23T17:57:47Z","title":"4D-LRM: Large Space-Time Reconstruction Model From and To Any View at Any Time","version":1},"reference_index":82,"source":"pdf_text","source_observed_at":"2026-08-15T18:46:46.199198Z"},"links":{"citing_paper":"/paper/2506.18890"},"observation_digest":"sha256:02d2d4bc0ab5ed11b55a0c769e4d62037668fb39665caf76447c95511fd2bff7","observation_id":"18222852-9626-4c42-9a91-cde7785b2f1c","resolution":{"observed_at":"2026-08-15T18:46:47.086679Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2503.16396","last_updated":"2025-03-25T02:07:12Z","snapshot_observed_at":"2026-08-16T12:48:11.162209Z","submitted_at":"2025-03-20T17:53:38Z","title":"SV4D 2.0: Enhancing Spatio-Temporal Consistency in Multi-View Video Diffusion for High-Quality 4D Generation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2503.16396","snapshot_observed_at":"2026-08-15T18:46:46.204844Z","title":"Sv4d 2.0: Enhancing spatio-temporal consistency in multi-view video diffusion for high-quality 4d generation.arXiv preprint arXiv:2503.16396, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.18890","last_updated":"2025-06-23T17:57:47Z","snapshot_observed_at":"2026-08-19T17:54:23.387474Z","submitted_at":"2025-06-23T17:57:47Z","title":"4D-LRM: Large Space-Time Reconstruction Model From and To Any View at Any Time","version":1},"reference_index":83,"source":"pdf_text","source_observed_at":"2026-08-15T18:46:46.204844Z"},"links":{"cited_paper":"/paper/2503.16396","citing_paper":"/paper/2506.18890"},"observation_digest":"sha256:1d68108160a9bd53ba1b4e084fe07237038e79f02bf2c598e994c2f53d205aa4","observation_id":"d60036bb-a32c-4128-bc11-b9c7a5684d29","resolution":{"observed_at":"2026-08-15T18:46:46.204844Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:46:47.062399Z","title":"Neural cages for detail-preserving 3d deformations","venue":null,"work_id":"9e35e5e2-8392-4e31-bbe3-2f3e2eb64875","year":2020},"citing_paper":{"arxiv_id":"2506.18890","last_updated":"2025-06-23T17:57:47Z","snapshot_observed_at":"2026-08-19T17:54:23.387474Z","submitted_at":"2025-06-23T17:57:47Z","title":"4D-LRM: Large Space-Time Reconstruction Model From and To Any View at Any Time","version":1},"reference_index":84,"source":"pdf_text","source_observed_at":"2026-08-15T18:46:46.210720Z"},"links":{"citing_paper":"/paper/2506.18890"},"observation_digest":"sha256:8541d7783ffff92daf3d4baf6942275298e03783e031631a14a9b0bdd634286f","observation_id":"570a1b81-2958-405c-88a8-9492901d6c36","resolution":{"observed_at":"2026-08-15T18:46:47.068185Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2312.17225","last_updated":"2024-12-04T06:11:50Z","snapshot_observed_at":"2026-08-19T15:22:00.036487Z","submitted_at":"2023-12-28T18:53:39Z","title":"4DGen: Grounded 4D Content Generation with Spatial-temporal Consistency","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2312.17225","snapshot_observed_at":"2026-08-15T18:46:46.215706Z","title":"4dgen: Grounded 4d content generation with spatial-temporal consistency.arXiv preprint arXiv:2312.17225, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.18890","last_updated":"2025-06-23T17:57:47Z","snapshot_observed_at":"2026-08-19T17:54:23.387474Z","submitted_at":"2025-06-23T17:57:47Z","title":"4D-LRM: Large Space-Time Reconstruction Model From and To Any View at Any Time","version":1},"reference_index":85,"source":"pdf_text","source_observed_at":"2026-08-15T18:46:46.215706Z"},"links":{"cited_paper":"/paper/2312.17225","citing_paper":"/paper/2506.18890"},"observation_digest":"sha256:0600c1f28da7b27060dbb5b3aa40617fed3df95f6b0089b8d69f90643557617e","observation_id":"959f6708-b342-4f80-8d13-1cf99554062c","resolution":{"observed_at":"2026-08-15T18:46:46.215706Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:46:46.221553Z","title":"pixelnerf: Neural radiance fields from one or few images","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.18890","last_updated":"2025-06-23T17:57:47Z","snapshot_observed_at":"2026-08-19T17:54:23.387474Z","submitted_at":"2025-06-23T17:57:47Z","title":"4D-LRM: Large Space-Time Reconstruction Model From and To Any View at Any Time","version":1},"reference_index":86,"source":"pdf_text","source_observed_at":"2026-08-15T18:46:46.221553Z"},"links":{"citing_paper":"/paper/2506.18890"},"observation_digest":"sha256:d18062289982cb3dfd25fe0e1953070c83da83888949505b622083c3e3b7ef7c","observation_id":"f2135c26-8969-4b85-8e28-d93e9b7bd7ea","resolution":{"observed_at":"2026-08-15T18:46:46.221553Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:46:46.226502Z","title":"Mip-splatting: Alias-free 3d gaussian splatting","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.18890","last_updated":"2025-06-23T17:57:47Z","snapshot_observed_at":"2026-08-19T17:54:23.387474Z","submitted_at":"2025-06-23T17:57:47Z","title":"4D-LRM: Large Space-Time Reconstruction Model From and To Any View at Any Time","version":1},"reference_index":87,"source":"pdf_text","source_observed_at":"2026-08-15T18:46:46.226502Z"},"links":{"citing_paper":"/paper/2506.18890"},"observation_digest":"sha256:953656c602ed07de57c3f2623a9a63fd20ff620fb82f66018c1ab510d35e17f7","observation_id":"df2cb3f3-ce55-4404-97ca-46145d80f296","resolution":{"observed_at":"2026-08-15T18:46:46.226502Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:46:47.019790Z","title":"Stag4d: Spatial-temporal anchored generative 4d gaussians","venue":null,"work_id":"06ea9978-47c2-464f-92ee-c419bc7a7255","year":2024},"citing_paper":{"arxiv_id":"2506.18890","last_updated":"2025-06-23T17:57:47Z","snapshot_observed_at":"2026-08-19T17:54:23.387474Z","submitted_at":"2025-06-23T17:57:47Z","title":"4D-LRM: Large Space-Time Reconstruction Model From and To Any View at Any Time","version":1},"reference_index":88,"source":"pdf_text","source_observed_at":"2026-08-15T18:46:46.231970Z"},"links":{"citing_paper":"/paper/2506.18890"},"observation_digest":"sha256:35a38a421ecc9fb60c646d17f449a13daea2e485e64ddc25c07c28e5dd13dd9f","observation_id":"ad0de8c2-dd7e-44fb-a74d-f8e9773ce3ce","resolution":{"observed_at":"2026-08-15T18:46:47.025348Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:46:47.000138Z","title":"4diffusion: Multi-view video diffusion model for 4d generation","venue":null,"work_id":"e5673002-c06a-4ae8-90c2-8bb9c80c824d","year":2024},"citing_paper":{"arxiv_id":"2506.18890","last_updated":"2025-06-23T17:57:47Z","snapshot_observed_at":"2026-08-19T17:54:23.387474Z","submitted_at":"2025-06-23T17:57:47Z","title":"4D-LRM: Large Space-Time Reconstruction Model From and To Any View at Any Time","version":1},"reference_index":89,"source":"pdf_text","source_observed_at":"2026-08-15T18:46:46.237752Z"},"links":{"citing_paper":"/paper/2506.18890"},"observation_digest":"sha256:7179380dc1bf13779957e2112ab24600f727aac06e4117f0599afba790007dd1","observation_id":"340551ee-8312-49d9-899f-342668555cf7","resolution":{"observed_at":"2026-08-15T18:46:47.007496Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:46:46.980420Z","title":"Monst3r: Asimpleapproachforestimatinggeometryinthe presence of motion","venue":null,"work_id":"c38606a4-22b1-42ec-92f2-bed8b15f236e","year":2025},"citing_paper":{"arxiv_id":"2506.18890","last_updated":"2025-06-23T17:57:47Z","snapshot_observed_at":"2026-08-19T17:54:23.387474Z","submitted_at":"2025-06-23T17:57:47Z","title":"4D-LRM: Large Space-Time Reconstruction Model From and To Any View at Any Time","version":1},"reference_index":90,"source":"pdf_text","source_observed_at":"2026-08-15T18:46:46.242982Z"},"links":{"citing_paper":"/paper/2506.18890"},"observation_digest":"sha256:5aa11bee8da31db314abfed57e860d43758e28a3cb765f6562176ce73977bf24","observation_id":"46185d18-90d1-4776-af91-0e0a562f4b25","resolution":{"observed_at":"2026-08-15T18:46:46.986914Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:46:46.961805Z","title":"Arf: Artistic radiance fields","venue":null,"work_id":"149df73a-547d-4820-9d71-a0a6dc7824ca","year":2022},"citing_paper":{"arxiv_id":"2506.18890","last_updated":"2025-06-23T17:57:47Z","snapshot_observed_at":"2026-08-19T17:54:23.387474Z","submitted_at":"2025-06-23T17:57:47Z","title":"4D-LRM: Large Space-Time Reconstruction Model From and To Any View at Any Time","version":1},"reference_index":91,"source":"pdf_text","source_observed_at":"2026-08-15T18:46:46.248037Z"},"links":{"citing_paper":"/paper/2506.18890"},"observation_digest":"sha256:b779e8dc2ff3cb4009e1ce65d56aae763dcbe8e4feae3c558c344c9346ef678b","observation_id":"af580e65-9c7b-406b-a01f-a869e515e0d7","resolution":{"observed_at":"2026-08-15T18:46:46.967089Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:46:46.943039Z","title":"Gs-lrm: Large reconstruction model for 3d gaussian splatting","venue":null,"work_id":"505978d8-2582-48af-9de3-fa583b071fb8","year":2024},"citing_paper":{"arxiv_id":"2506.18890","last_updated":"2025-06-23T17:57:47Z","snapshot_observed_at":"2026-08-19T17:54:23.387474Z","submitted_at":"2025-06-23T17:57:47Z","title":"4D-LRM: Large Space-Time Reconstruction Model From and To Any View at Any Time","version":1},"reference_index":92,"source":"pdf_text","source_observed_at":"2026-08-15T18:46:46.253403Z"},"links":{"citing_paper":"/paper/2506.18890"},"observation_digest":"sha256:c890efda666a44f84ba1cf333c671ba9e402679020eca1064d6cce974af24820","observation_id":"a48019bf-6760-483a-acae-96d1bb97114f","resolution":{"observed_at":"2026-08-15T18:46:46.949522Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:46:46.924995Z","title":"Theunreasonable effectiveness of deep features as a perceptual metric","venue":null,"work_id":"f2a03b84-4921-44a4-8033-f0334cf67f8e","year":2018},"citing_paper":{"arxiv_id":"2506.18890","last_updated":"2025-06-23T17:57:47Z","snapshot_observed_at":"2026-08-19T17:54:23.387474Z","submitted_at":"2025-06-23T17:57:47Z","title":"4D-LRM: Large Space-Time Reconstruction Model From and To Any View at Any Time","version":1},"reference_index":93,"source":"pdf_text","source_observed_at":"2026-08-15T18:46:46.258482Z"},"links":{"citing_paper":"/paper/2506.18890"},"observation_digest":"sha256:25f09518d7b95ff891c729f912fb028f27609d6d118c399a8ad18aa1e8b25b69","observation_id":"eabfd8c9-ad7e-4f93-809e-000b775206ac","resolution":{"observed_at":"2026-08-15T18:46:46.931311Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2505.23884","last_updated":"2025-05-29T17:50:34Z","snapshot_observed_at":"2026-08-08T17:47:51.120243Z","submitted_at":"2025-05-29T17:50:34Z","title":"Test-Time Training Done Right","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2505.23884","snapshot_observed_at":"2026-08-15T18:46:46.263199Z","title":"Test-time training done right.arXiv preprint arXiv:2505.23884, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.18890","last_updated":"2025-06-23T17:57:47Z","snapshot_observed_at":"2026-08-19T17:54:23.387474Z","submitted_at":"2025-06-23T17:57:47Z","title":"4D-LRM: Large Space-Time Reconstruction Model From and To Any View at Any Time","version":1},"reference_index":94,"source":"pdf_text","source_observed_at":"2026-08-15T18:46:46.263199Z"},"links":{"cited_paper":"/paper/2505.23884","citing_paper":"/paper/2506.18890"},"observation_digest":"sha256:94b8ce06ebb81d7eb042dc426039d680a9bbb8b0a01a07080bd1c2d4cf5ab4ac","observation_id":"784135fd-4ed2-4a03-95b9-c54a0dcfefaf","resolution":{"observed_at":"2026-08-15T18:46:46.263199Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:46:46.907476Z","title":"Susskind, and Alex Schwing","venue":null,"work_id":"164a41cb-5eb1-48de-b3ec-073fda11100f","year":2024},"citing_paper":{"arxiv_id":"2506.18890","last_updated":"2025-06-23T17:57:47Z","snapshot_observed_at":"2026-08-19T17:54:23.387474Z","submitted_at":"2025-06-23T17:57:47Z","title":"4D-LRM: Large Space-Time Reconstruction Model From and To Any View at Any Time","version":1},"reference_index":95,"source":"pdf_text","source_observed_at":"2026-08-15T18:46:46.268365Z"},"links":{"citing_paper":"/paper/2506.18890"},"observation_digest":"sha256:2284be5761968b51f2b75c3ef4ea537ab997ad9e867f0495ed6a7b4561616605","observation_id":"1cc8e86b-bf4c-41cb-b7a0-81c0386850ed","resolution":{"observed_at":"2026-08-15T18:46:46.912996Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2504.20995","last_updated":"2025-04-29T17:59:30Z","snapshot_observed_at":"2026-08-18T18:18:49.891258Z","submitted_at":"2025-04-29T17:59:30Z","title":"TesserAct: Learning 4D Embodied World Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2504.20995","snapshot_observed_at":"2026-08-15T18:46:46.273244Z","title":"Tesseract: Learning 4d embodied world models.arXiv preprint arXiv:2504.20995, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.18890","last_updated":"2025-06-23T17:57:47Z","snapshot_observed_at":"2026-08-19T17:54:23.387474Z","submitted_at":"2025-06-23T17:57:47Z","title":"4D-LRM: Large Space-Time Reconstruction Model From and To Any View at Any Time","version":1},"reference_index":96,"source":"pdf_text","source_observed_at":"2026-08-15T18:46:46.273244Z"},"links":{"cited_paper":"/paper/2504.20995","citing_paper":"/paper/2506.18890"},"observation_digest":"sha256:4541f3e9e0ee7bd9ccbf1eefcf093ba31059d3ccc3137efafd046fccea359cd8","observation_id":"857aedae-a710-4d7b-82a8-ff8c69b8583d","resolution":{"observed_at":"2026-08-15T18:46:46.273244Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:46:46.887504Z","title":"Drivable 3d gaussian avatars","venue":null,"work_id":"34f7f2cc-dbc4-47b8-b19b-3c5a5447e915","year":2025},"citing_paper":{"arxiv_id":"2506.18890","last_updated":"2025-06-23T17:57:47Z","snapshot_observed_at":"2026-08-19T17:54:23.387474Z","submitted_at":"2025-06-23T17:57:47Z","title":"4D-LRM: Large Space-Time Reconstruction Model From and To Any View at Any Time","version":1},"reference_index":97,"source":"pdf_text","source_observed_at":"2026-08-15T18:46:46.278617Z"},"links":{"citing_paper":"/paper/2506.18890"},"observation_digest":"sha256:5595c583146fd582eb1dcf09c27955c0844edf5924c19c303bc1a84325f1659e","observation_id":"3b140970-a13d-43be-ab82-98fc88a46a76","resolution":{"observed_at":"2026-08-15T18:46:46.894246Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2410.12781","last_updated":"2025-08-01T04:29:18Z","snapshot_observed_at":"2026-08-18T10:13:51.230592Z","submitted_at":"2024-10-16T17:54:06Z","title":"Long-LRM: Long-sequence Large Reconstruction Model for Wide-coverage Gaussian Splats","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.12781","snapshot_observed_at":"2026-08-15T18:46:46.283585Z","title":"Long-lrm: Long-sequence large reconstruction model for wide-coverage gaussian splats.arXiv preprint arXiv:2410.12781, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.18890","last_updated":"2025-06-23T17:57:47Z","snapshot_observed_at":"2026-08-19T17:54:23.387474Z","submitted_at":"2025-06-23T17:57:47Z","title":"4D-LRM: Large Space-Time Reconstruction Model From and To Any View at Any Time","version":1},"reference_index":98,"source":"pdf_text","source_observed_at":"2026-08-15T18:46:46.283585Z"},"links":{"cited_paper":"/paper/2410.12781","citing_paper":"/paper/2506.18890"},"observation_digest":"sha256:9ebce4e540be1b0a45b310e57009f0b20aabfc6f17f341a7dc49e6c3957ee1ca","observation_id":"d84ebe43-3112-4466-920f-501d32f51bf4","resolution":{"observed_at":"2026-08-15T18:46:46.283585Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2506.18890","last_updated":"2025-06-23T17:57:47Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-19T17:54:23.387474Z","submitted_at":"2025-06-23T17:57:47Z","title":"4D-LRM: Large Space-Time Reconstruction Model From and To Any View at Any Time"},"reference_resolution":{"displayed":98,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":51,"verified_exact":0,"verified_fuzzy":47},"total_outbound_references":98},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"thesis":"As of 19 August 2026, this Paper Citation Record lists 98 of 98 outbound references and 8 inbound Pith citation observations for arXiv:2506.18890."}