{"as_of":"2026-08-13T14:14:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:ea52fcfa08394dc97c9a30980453231f4dd39a7002bae94609b5ecf2e0e12a10","coverage":[{"denominator":113,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":100,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-11T23:13:39.767834Z","state":"measured"},{"denominator":101,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":101,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-13T06:32:02.005865+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-05-19T07:25:41.219753Z","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":"2026-05-19T07:27:08.948579Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2412.02690","last_updated":"2024-12-04T20:51:17Z","snapshot_observed_at":"2026-08-12T15:54:02.294599Z","submitted_at":"2024-12-03T18:58:19Z","title":"FoundHand: Large-Scale Domain-Specific Learning for Controllable Hand Image Generation","version":2},"cited_work":{"arxiv_id":"2412.02690","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2412.02690","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Foundhand: Large- scale domain-specific learning for controllable hand image generation","venue":null,"work_id":"f804423a-1051-4db4-ba39-3d5ea94a79a9","year":2024},"citing_paper":{"arxiv_id":"2506.19840","last_updated":"2026-04-16T20:25:04Z","snapshot_observed_at":"2026-08-08T19:39:46.308514Z","submitted_at":"2025-06-24T17:58:04Z","title":"GenHSI: Controllable Generation of Human-Scene Interaction Videos","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-05-19T07:25:41.219753Z"},"links":{"cited_paper":"/paper/2412.02690","citing_paper":"/paper/2506.19840"},"observation_digest":"sha256:4d5512c5724c9f811f4fcad8014f5074edffa7a49c8fe074a093ef804a718bdd","observation_id":"8980f562-7d33-4e49-909f-b8303dd47069","resolution":{"observed_at":"2026-05-19T07:27:08.950681Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2412.02690/citation-record","integrity":"/paper/2412.02690/integrity","json":"/paper/2412.02690/citation-record.json","paper":"/paper/2412.02690"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:13:39.309705Z","title":"https : / / www","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.02690","last_updated":"2024-12-04T20:51:17Z","snapshot_observed_at":"2026-08-12T15:54:02.294599Z","submitted_at":"2024-12-03T18:58:19Z","title":"FoundHand: Large-Scale Domain-Specific Learning for Controllable Hand Image Generation","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-11T23:13:39.309705Z"},"links":{"citing_paper":"/paper/2412.02690"},"observation_digest":"sha256:c25d885c1da277c14965a62caaf4563cf7e957433ec4b41777d390873c5b24ad","observation_id":"f78d3800-7ff9-4867-967e-22eefce87eed","resolution":{"observed_at":"2026-08-11T23:13:39.309705Z","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":"2026-08-11T23:13:39.315074Z","title":"https : //www.youtube.com/watch?v=24yjRbBah3w ,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.02690","last_updated":"2024-12-04T20:51:17Z","snapshot_observed_at":"2026-08-12T15:54:02.294599Z","submitted_at":"2024-12-03T18:58:19Z","title":"FoundHand: Large-Scale Domain-Specific Learning for Controllable Hand Image Generation","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-11T23:13:39.315074Z"},"links":{"citing_paper":"/paper/2412.02690"},"observation_digest":"sha256:7c7547263e004dc496a7b7396a7cf9e04cbcc402f50a7e059d4b315f023c2415","observation_id":"fd71576e-b2a2-4a1b-b5e5-cd645393a3d6","resolution":{"observed_at":"2026-08-11T23:13:39.315074Z","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":"2026-08-11T23:13:39.319510Z","title":"https : //www.reddit.com/r/AskReddit/comments/ y8oe9l/why_ai_cant_draw_hands/ ,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.02690","last_updated":"2024-12-04T20:51:17Z","snapshot_observed_at":"2026-08-12T15:54:02.294599Z","submitted_at":"2024-12-03T18:58:19Z","title":"FoundHand: Large-Scale Domain-Specific Learning for Controllable Hand Image Generation","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-11T23:13:39.319510Z"},"links":{"citing_paper":"/paper/2412.02690"},"observation_digest":"sha256:699210f86febbfca8cb970437e8a6204671b13470a0ab18023dbeebd9d494d21","observation_id":"c512e7a7-4787-4518-b324-ec36e45db201","resolution":{"observed_at":"2026-08-11T23:13:39.319510Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2109.06166","last_updated":"2021-09-13T17:59:33Z","snapshot_observed_at":"2026-08-13T00:06:38.037811Z","submitted_at":"2021-09-13T17:59:33Z","title":"Pose with Style: Detail-Preserving Pose-Guided Image Synthesis with Conditional StyleGAN","version":1},"cited_work":{"arxiv_id":"2109.06166","doi":null,"metadata_source":"pith","pith_arxiv_id":"2109.06166","snapshot_observed_at":"2026-08-11T23:13:40.464258Z","title":"Pose with Style: Detail-Preserving Pose-Guided Image Synthesis with Conditional StyleGAN","venue":"cs.CV","work_id":"950951bc-41da-4f62-8089-bb675d26d773","year":2021},"citing_paper":{"arxiv_id":"2412.02690","last_updated":"2024-12-04T20:51:17Z","snapshot_observed_at":"2026-08-12T15:54:02.294599Z","submitted_at":"2024-12-03T18:58:19Z","title":"FoundHand: Large-Scale Domain-Specific Learning for Controllable Hand Image Generation","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-11T23:13:39.324249Z"},"links":{"cited_paper":"/paper/2109.06166","citing_paper":"/paper/2412.02690"},"observation_digest":"sha256:6d7e6657ab1c9191d646c285bc03f147381bdb99716114e9e17b6c9e1d6fe183","observation_id":"62a172d4-506c-42cf-a891-04c0786c51d2","resolution":{"observed_at":"2026-08-11T23:13:40.469875Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T23:13:39.329308Z","title":"Renderdiffusion: Image diffusion for 3d reconstruction, in- painting and generation","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.02690","last_updated":"2024-12-04T20:51:17Z","snapshot_observed_at":"2026-08-12T15:54:02.294599Z","submitted_at":"2024-12-03T18:58:19Z","title":"FoundHand: Large-Scale Domain-Specific Learning for Controllable Hand Image Generation","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-11T23:13:39.329308Z"},"links":{"citing_paper":"/paper/2412.02690"},"observation_digest":"sha256:b168f105b2199ee5baaffe7c8d623a4a838345f048e351c39b554bd16ab3a8cc","observation_id":"ab4b7594-cd74-4d54-ab16-317a00658335","resolution":{"observed_at":"2026-08-11T23:13:39.329308Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2309.07986","last_updated":"2024-07-26T11:14:21Z","snapshot_observed_at":"2026-08-13T10:13:24.864524Z","submitted_at":"2023-09-14T18:52:16Z","title":"Viewpoint Textual Inversion: Discovering Scene Representations and 3D View Control in 2D Diffusion Models","version":2},"cited_work":{"arxiv_id":"2309.07986","doi":null,"metadata_source":"pith","pith_arxiv_id":"2309.07986","snapshot_observed_at":"2026-08-11T23:13:40.442145Z","title":"Viewpoint Textual Inversion: Discovering Scene Representations and 3D View Control in 2D Diffusion Models","venue":"cs.CV","work_id":"e816eca9-7381-4f94-b2f5-31b12035262c","year":2023},"citing_paper":{"arxiv_id":"2412.02690","last_updated":"2024-12-04T20:51:17Z","snapshot_observed_at":"2026-08-12T15:54:02.294599Z","submitted_at":"2024-12-03T18:58:19Z","title":"FoundHand: Large-Scale Domain-Specific Learning for Controllable Hand Image Generation","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-11T23:13:39.333789Z"},"links":{"cited_paper":"/paper/2309.07986","citing_paper":"/paper/2412.02690"},"observation_digest":"sha256:7f6e5058ed3337d08872d71c7b5ba3b18fadc0969b6b81b4b19b7589f6045258","observation_id":"9d322d04-5c11-43af-801c-fe958cf52c4c","resolution":{"observed_at":"2026-08-11T23:13:40.447478Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T23:13:39.338750Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2412.02690","last_updated":"2024-12-04T20:51:17Z","snapshot_observed_at":"2026-08-12T15:54:02.294599Z","submitted_at":"2024-12-03T18:58:19Z","title":"FoundHand: Large-Scale Domain-Specific Learning for Controllable Hand Image Generation","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-11T23:13:39.338750Z"},"links":{"citing_paper":"/paper/2412.02690"},"observation_digest":"sha256:4b2b5776ba539862af2399ed7a97d86e0ff5730be3ca3c584da93ce53bab79d3","observation_id":"cb52b71a-8175-4465-9841-c778ecf08a12","resolution":{"observed_at":"2026-08-11T23:13:39.338750Z","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":"2026-08-11T23:13:39.342692Z","title":"Efficient geometry-aware 3d generative adversarial networks","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.02690","last_updated":"2024-12-04T20:51:17Z","snapshot_observed_at":"2026-08-12T15:54:02.294599Z","submitted_at":"2024-12-03T18:58:19Z","title":"FoundHand: Large-Scale Domain-Specific Learning for Controllable Hand Image Generation","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-11T23:13:39.342692Z"},"links":{"citing_paper":"/paper/2412.02690"},"observation_digest":"sha256:8a08f29c6f0ed608c4fdc2ad9a9ca6e2683694467034bf9e188d123f6e32ae59","observation_id":"2d27f082-8595-4a63-8db0-b60e96fbf2f7","resolution":{"observed_at":"2026-08-11T23:13:39.342692Z","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":"2026-08-11T23:13:39.346610Z","title":"Chan, Koki Nagano, Matthew A","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.02690","last_updated":"2024-12-04T20:51:17Z","snapshot_observed_at":"2026-08-12T15:54:02.294599Z","submitted_at":"2024-12-03T18:58:19Z","title":"FoundHand: Large-Scale Domain-Specific Learning for Controllable Hand Image Generation","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-11T23:13:39.346610Z"},"links":{"citing_paper":"/paper/2412.02690"},"observation_digest":"sha256:b959b3040c18fa85520a29c55a94481e0d6958bfc5b4b4b13a1ef941f2d1fb99","observation_id":"ca3c1646-f7e2-4f56-be12-59328fbc7a47","resolution":{"observed_at":"2026-08-11T23:13:39.346610Z","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":"2026-08-11T23:13:39.350743Z","title":"Narang, Karl Van Wyk, Umar Iqbal, Stan Birchfield, Jan Kautz, and Di- eter Fox","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.02690","last_updated":"2024-12-04T20:51:17Z","snapshot_observed_at":"2026-08-12T15:54:02.294599Z","submitted_at":"2024-12-03T18:58:19Z","title":"FoundHand: Large-Scale Domain-Specific Learning for Controllable Hand Image Generation","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-11T23:13:39.350743Z"},"links":{"citing_paper":"/paper/2412.02690"},"observation_digest":"sha256:e96be4c3b1553ed3197e3ff514a282cadb8b75ffd01e6e6c6f8c3b552cd4c606","observation_id":"4516f8c6-3012-4ca7-a510-67366e49feb6","resolution":{"observed_at":"2026-08-11T23:13:39.350743Z","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":"2026-08-11T23:13:39.354599Z","title":"Unpaired pose guided human image generation","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2412.02690","last_updated":"2024-12-04T20:51:17Z","snapshot_observed_at":"2026-08-12T15:54:02.294599Z","submitted_at":"2024-12-03T18:58:19Z","title":"FoundHand: Large-Scale Domain-Specific Learning for Controllable Hand Image Generation","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-11T23:13:39.354599Z"},"links":{"citing_paper":"/paper/2412.02690"},"observation_digest":"sha256:a34cf2f8ede50302d7da4c23b8f1b4872b49d473d661a4ce2032ff110d4ef453","observation_id":"f1e28410-319a-488b-902c-80a229b915ec","resolution":{"observed_at":"2026-08-11T23:13:39.354599Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.10167","last_updated":"2024-06-14T16:38:00Z","snapshot_observed_at":"2026-08-12T23:42:32.658551Z","submitted_at":"2024-06-14T16:38:00Z","title":"4DRecons: 4D Neural Implicit Deformable Objects Reconstruction from a single RGB-D Camera with Geometrical and Topological Regularizations","version":1},"cited_work":{"arxiv_id":"2406.10167","doi":null,"metadata_source":"pith","pith_arxiv_id":"2406.10167","snapshot_observed_at":"2026-08-11T23:13:40.420239Z","title":"4DRecons: 4D Neural Implicit Deformable Objects Reconstruction from a single RGB-D Camera with Geometrical and Topological Regularizations","venue":"cs.CV","work_id":"9de2dad6-8ba1-4995-972b-3744b00f89ef","year":2024},"citing_paper":{"arxiv_id":"2412.02690","last_updated":"2024-12-04T20:51:17Z","snapshot_observed_at":"2026-08-12T15:54:02.294599Z","submitted_at":"2024-12-03T18:58:19Z","title":"FoundHand: Large-Scale Domain-Specific Learning for Controllable Hand Image Generation","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-11T23:13:39.358906Z"},"links":{"cited_paper":"/paper/2406.10167","citing_paper":"/paper/2412.02690"},"observation_digest":"sha256:aeafba6fa9dc49cef113244a8947f7cc3acfd436459204ba999c1847fa48a807","observation_id":"ea09f95a-979d-4207-a4fc-ca29000406b1","resolution":{"observed_at":"2026-08-11T23:13:40.425607Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T23:13:39.363563Z","title":"Rescaling egocentric vision: Collection, pipeline and chal- lenges for epic-kitchens-100","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.02690","last_updated":"2024-12-04T20:51:17Z","snapshot_observed_at":"2026-08-12T15:54:02.294599Z","submitted_at":"2024-12-03T18:58:19Z","title":"FoundHand: Large-Scale Domain-Specific Learning for Controllable Hand Image Generation","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-11T23:13:39.363563Z"},"links":{"citing_paper":"/paper/2412.02690"},"observation_digest":"sha256:85d946fb3c6dee0f0b62f1a738d28fd2c17da30ca14b13534679b8f518393dda","observation_id":"e3855e28-0dcd-47f8-8bef-18f2cf7be92f","resolution":{"observed_at":"2026-08-11T23:13:39.363563Z","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":"2026-08-11T23:13:39.367930Z","title":"Imagenet: A large-scale hierarchical image database","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2412.02690","last_updated":"2024-12-04T20:51:17Z","snapshot_observed_at":"2026-08-12T15:54:02.294599Z","submitted_at":"2024-12-03T18:58:19Z","title":"FoundHand: Large-Scale Domain-Specific Learning for Controllable Hand Image Generation","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-11T23:13:39.367930Z"},"links":{"citing_paper":"/paper/2412.02690"},"observation_digest":"sha256:6c0c23e636950bad114d81f93503c7b62f89af09679fb9879114a78edceb14ea","observation_id":"17caaad6-b3c5-4ca4-a42d-ecd84b56dba2","resolution":{"observed_at":"2026-08-11T23:13:39.367930Z","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":"2026-08-11T23:13:39.372509Z","title":"Disentangled and controllable face image genera- tion via 3d imitative-contrastive learning","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2412.02690","last_updated":"2024-12-04T20:51:17Z","snapshot_observed_at":"2026-08-12T15:54:02.294599Z","submitted_at":"2024-12-03T18:58:19Z","title":"FoundHand: Large-Scale Domain-Specific Learning for Controllable Hand Image Generation","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-11T23:13:39.372509Z"},"links":{"citing_paper":"/paper/2412.02690"},"observation_digest":"sha256:522fbd7b0897de101f3402a75798fc0340003f1642b09efa8da96c1debb7c722","observation_id":"ff810ff3-331a-4058-a1f6-85d27fc82fdf","resolution":{"observed_at":"2026-08-11T23:13:39.372509Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1810.04805","last_updated":"2019-05-24T20:37:26Z","snapshot_observed_at":"2026-07-30T09:12:38.100527Z","submitted_at":"2018-10-11T00:50:01Z","title":"BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1810.04805","snapshot_observed_at":"2026-08-11T23:13:39.376805Z","title":"Bert: Pre-training of deep bidirectional transformers for language understanding","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2412.02690","last_updated":"2024-12-04T20:51:17Z","snapshot_observed_at":"2026-08-12T15:54:02.294599Z","submitted_at":"2024-12-03T18:58:19Z","title":"FoundHand: Large-Scale Domain-Specific Learning for Controllable Hand Image Generation","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-11T23:13:39.376805Z"},"links":{"cited_paper":"/paper/1810.04805","citing_paper":"/paper/2412.02690"},"observation_digest":"sha256:4f1376b7c15665de618cee9026d013ced3d983e4ad8273ed39a2fc8bc193a7b8","observation_id":"0e922358-2f0c-4044-adb1-be24e7c38c54","resolution":{"observed_at":"2026-08-11T23:13:39.376805Z","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":"2026-08-11T23:13:39.381257Z","title":"Diffusion models beat gans on image synthesis","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.02690","last_updated":"2024-12-04T20:51:17Z","snapshot_observed_at":"2026-08-12T15:54:02.294599Z","submitted_at":"2024-12-03T18:58:19Z","title":"FoundHand: Large-Scale Domain-Specific Learning for Controllable Hand Image Generation","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-11T23:13:39.381257Z"},"links":{"citing_paper":"/paper/2412.02690"},"observation_digest":"sha256:19a45fb2db95366569fb5b37360751a2f56c5f9e3e312a7fb7248a4aa381d3c4","observation_id":"c74e349c-a5b1-4e76-a376-e5c48e0c31fd","resolution":{"observed_at":"2026-08-11T23:13:39.381257Z","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":"2026-08-11T23:13:39.385828Z","title":"Scalable pre-training of large autoregressive image models, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.02690","last_updated":"2024-12-04T20:51:17Z","snapshot_observed_at":"2026-08-12T15:54:02.294599Z","submitted_at":"2024-12-03T18:58:19Z","title":"FoundHand: Large-Scale Domain-Specific Learning for Controllable Hand Image Generation","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-11T23:13:39.385828Z"},"links":{"citing_paper":"/paper/2412.02690"},"observation_digest":"sha256:fecd3fd2b5c915c8c982ebacd746722c9fe3305442eb00f7945c976d5f566c50","observation_id":"54fb19f5-9403-4870-b659-1c72c4a6336c","resolution":{"observed_at":"2026-08-11T23:13:39.385828Z","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":"2026-08-11T23:13:39.390340Z","title":"Black, and Otmar Hilliges","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.02690","last_updated":"2024-12-04T20:51:17Z","snapshot_observed_at":"2026-08-12T15:54:02.294599Z","submitted_at":"2024-12-03T18:58:19Z","title":"FoundHand: Large-Scale Domain-Specific Learning for Controllable Hand Image Generation","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-11T23:13:39.390340Z"},"links":{"citing_paper":"/paper/2412.02690"},"observation_digest":"sha256:eee698a4f61ef0b11d410c1fe13f768c7695208179c9d22c1f80f2f1a38281c7","observation_id":"2ffa9d39-526a-433b-8185-866c82768857","resolution":{"observed_at":"2026-08-11T23:13:39.390340Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.10718","last_updated":"2025-04-30T02:19:25Z","snapshot_observed_at":"2026-08-13T00:05:11.605959Z","submitted_at":"2024-05-17T12:01:43Z","title":"SignLLM: Sign Language Production Large Language Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.10718","snapshot_observed_at":"2026-08-11T23:13:39.394635Z","title":"Signllm: Sign languages production large language models","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.02690","last_updated":"2024-12-04T20:51:17Z","snapshot_observed_at":"2026-08-12T15:54:02.294599Z","submitted_at":"2024-12-03T18:58:19Z","title":"FoundHand: Large-Scale Domain-Specific Learning for Controllable Hand Image Generation","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-11T23:13:39.394635Z"},"links":{"cited_paper":"/paper/2405.10718","citing_paper":"/paper/2412.02690"},"observation_digest":"sha256:83d60501c2582aecb24725016a4c88b8d016b5048ad4a4c8abcbe2a73dc50ced","observation_id":"2e9b82ee-30bd-4dbb-8d5b-5a09c3258f0a","resolution":{"observed_at":"2026-08-11T23:13:39.394635Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2312.05107","last_updated":"2023-12-11T11:00:13Z","snapshot_observed_at":"2026-08-13T05:07:11.609869Z","submitted_at":"2023-12-08T15:37:17Z","title":"DreaMoving: A Human Video Generation Framework based on Diffusion Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2312.05107","snapshot_observed_at":"2026-08-11T23:13:39.399358Z","title":"Dreamoving: A human dance video genera- tion framework based on diffusion models","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.02690","last_updated":"2024-12-04T20:51:17Z","snapshot_observed_at":"2026-08-12T15:54:02.294599Z","submitted_at":"2024-12-03T18:58:19Z","title":"FoundHand: Large-Scale Domain-Specific Learning for Controllable Hand Image Generation","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-11T23:13:39.399358Z"},"links":{"cited_paper":"/paper/2312.05107","citing_paper":"/paper/2412.02690"},"observation_digest":"sha256:3c839f36dfe0de37e295e9e166c0a00fc2781dd3d5d69d37e5e3c2186dee5160","observation_id":"1e9d2733-fba6-490a-aacb-93f5261a68e9","resolution":{"observed_at":"2026-08-11T23:13:39.399358Z","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":"2026-08-11T23:13:39.404300Z","title":"Dart: Articulated hand model with diverse accessories and rich textures","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.02690","last_updated":"2024-12-04T20:51:17Z","snapshot_observed_at":"2026-08-12T15:54:02.294599Z","submitted_at":"2024-12-03T18:58:19Z","title":"FoundHand: Large-Scale Domain-Specific Learning for Controllable Hand Image Generation","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-11T23:13:39.404300Z"},"links":{"citing_paper":"/paper/2412.02690"},"observation_digest":"sha256:d82ca014fab8933f8d66b8f24b4e69baf1b0b2d8f82e68d93fb9b9e71c162601","observation_id":"1a42e2e7-61d6-4d18-94b4-013a320d8701","resolution":{"observed_at":"2026-08-11T23:13:39.404300Z","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":"2026-08-11T23:13:39.408705Z","title":"Vivid-1-to-3: Novel view synthesis with video diffusion models, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.02690","last_updated":"2024-12-04T20:51:17Z","snapshot_observed_at":"2026-08-12T15:54:02.294599Z","submitted_at":"2024-12-03T18:58:19Z","title":"FoundHand: Large-Scale Domain-Specific Learning for Controllable Hand Image Generation","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-11T23:13:39.408705Z"},"links":{"citing_paper":"/paper/2412.02690"},"observation_digest":"sha256:c8a16f78b66788b34473bcd841968d0432c4afef8734f52a8332014839ac24b4","observation_id":"5667f851-ff32-4828-ad5b-b38bec39d6c5","resolution":{"observed_at":"2026-08-11T23:13:39.408705Z","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":"2026-08-11T23:13:39.413288Z","title":"Generative adversarial nets","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2412.02690","last_updated":"2024-12-04T20:51:17Z","snapshot_observed_at":"2026-08-12T15:54:02.294599Z","submitted_at":"2024-12-03T18:58:19Z","title":"FoundHand: Large-Scale Domain-Specific Learning for Controllable Hand Image Generation","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-11T23:13:39.413288Z"},"links":{"citing_paper":"/paper/2412.02690"},"observation_digest":"sha256:35c295cd0b66e2bf7108e78b385afb7d5ea00e0b60230a79b2cd830929ad8606","observation_id":"caa081b5-8068-4414-98df-422dc13d72bc","resolution":{"observed_at":"2026-08-11T23:13:39.413288Z","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":"2026-08-11T23:13:39.417968Z","title":"Ego4d: Around the world in 3,000 hours of egocentric video","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.02690","last_updated":"2024-12-04T20:51:17Z","snapshot_observed_at":"2026-08-12T15:54:02.294599Z","submitted_at":"2024-12-03T18:58:19Z","title":"FoundHand: Large-Scale Domain-Specific Learning for Controllable Hand Image Generation","version":2},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-11T23:13:39.417968Z"},"links":{"citing_paper":"/paper/2412.02690"},"observation_digest":"sha256:7e3da6000da49e2bd19d60832f0f16083e113825473da792c892c214449037b7","observation_id":"1e0bf663-727f-43a3-ad48-3c56be94224e","resolution":{"observed_at":"2026-08-11T23:13:39.417968Z","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":"2026-08-11T23:13:39.422778Z","title":"Effects of hand representations for typing in virtual reality","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2412.02690","last_updated":"2024-12-04T20:51:17Z","snapshot_observed_at":"2026-08-12T15:54:02.294599Z","submitted_at":"2024-12-03T18:58:19Z","title":"FoundHand: Large-Scale Domain-Specific Learning for Controllable Hand Image Generation","version":2},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-11T23:13:39.422778Z"},"links":{"citing_paper":"/paper/2412.02690"},"observation_digest":"sha256:de7bf7d6c65760a547ea72927d33b51c4cba379d697069af5fbad8d58e9e9be8","observation_id":"6441cd99-eb68-4c17-9b4e-ab0b29fc1d69","resolution":{"observed_at":"2026-08-11T23:13:39.422778Z","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":"2026-08-11T23:13:39.427093Z","title":"Sparsectrl: Adding sparse controls to text-to-video diffusion models, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.02690","last_updated":"2024-12-04T20:51:17Z","snapshot_observed_at":"2026-08-12T15:54:02.294599Z","submitted_at":"2024-12-03T18:58:19Z","title":"FoundHand: Large-Scale Domain-Specific Learning for Controllable Hand Image Generation","version":2},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-11T23:13:39.427093Z"},"links":{"citing_paper":"/paper/2412.02690"},"observation_digest":"sha256:a9e112052929541df07a58bd63790c5903d5a55707ec9e22b07b3c3888d07bc8","observation_id":"8824dae1-b78c-4b8e-a7b2-0a897538a0f6","resolution":{"observed_at":"2026-08-11T23:13:39.427093Z","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":"2026-08-11T23:13:39.431385Z","title":"Classifier-free diffusion guidance, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.02690","last_updated":"2024-12-04T20:51:17Z","snapshot_observed_at":"2026-08-12T15:54:02.294599Z","submitted_at":"2024-12-03T18:58:19Z","title":"FoundHand: Large-Scale Domain-Specific Learning for Controllable Hand Image Generation","version":2},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-11T23:13:39.431385Z"},"links":{"citing_paper":"/paper/2412.02690"},"observation_digest":"sha256:5f726a29d2e0f2e3135d813b07a6fa1c06c4adb4623b61cd7348643cc02f4fe2","observation_id":"010aa7a2-b246-4f7d-a760-11fcf25882ff","resolution":{"observed_at":"2026-08-11T23:13:39.431385Z","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":"2026-08-11T23:13:39.435666Z","title":"Denoising dif- fusion probabilistic models","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2412.02690","last_updated":"2024-12-04T20:51:17Z","snapshot_observed_at":"2026-08-12T15:54:02.294599Z","submitted_at":"2024-12-03T18:58:19Z","title":"FoundHand: Large-Scale Domain-Specific Learning for Controllable Hand Image Generation","version":2},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-11T23:13:39.435666Z"},"links":{"citing_paper":"/paper/2412.02690"},"observation_digest":"sha256:6585b56b4f8f21c8712157e90aa8b1722094aa8df2e60758440e2e7ca068d840","observation_id":"c0113cd5-4be9-4591-8d4d-0dd0126e37b8","resolution":{"observed_at":"2026-08-11T23:13:39.435666Z","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":"2026-08-11T23:13:39.440196Z","title":"Model-aware gesture-to-gesture transla- tion","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.02690","last_updated":"2024-12-04T20:51:17Z","snapshot_observed_at":"2026-08-12T15:54:02.294599Z","submitted_at":"2024-12-03T18:58:19Z","title":"FoundHand: Large-Scale Domain-Specific Learning for Controllable Hand Image Generation","version":2},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-11T23:13:39.440196Z"},"links":{"citing_paper":"/paper/2412.02690"},"observation_digest":"sha256:2b68ea48ffa4af16d6b87b1e403df5662920632096a53ecba5f16f8ef0aecd6e","observation_id":"86a8e38b-2ce5-48af-a9ce-469a54a34817","resolution":{"observed_at":"2026-08-11T23:13:39.440196Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2311.17117","last_updated":"2024-06-13T06:37:20Z","snapshot_observed_at":"2026-08-13T05:15:29.772394Z","submitted_at":"2023-11-28T12:27:15Z","title":"Animate Anyone: Consistent and Controllable Image-to-Video Synthesis for Character Animation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2311.17117","snapshot_observed_at":"2026-08-11T23:13:39.444597Z","title":"Animate anyone: Consistent and controllable image-to-video synthesis for character animation","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.02690","last_updated":"2024-12-04T20:51:17Z","snapshot_observed_at":"2026-08-12T15:54:02.294599Z","submitted_at":"2024-12-03T18:58:19Z","title":"FoundHand: Large-Scale Domain-Specific Learning for Controllable Hand Image Generation","version":2},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-11T23:13:39.444597Z"},"links":{"cited_paper":"/paper/2311.17117","citing_paper":"/paper/2412.02690"},"observation_digest":"sha256:3ac7baeb339baf577e5d1fd9526a58355397afd76ae0745d8e3bb78b996fcf17","observation_id":"88b92e8e-30e0-4d75-ad36-000bc9617c18","resolution":{"observed_at":"2026-08-11T23:13:39.444597Z","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":"2026-08-11T23:13:39.449417Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2412.02690","last_updated":"2024-12-04T20:51:17Z","snapshot_observed_at":"2026-08-12T15:54:02.294599Z","submitted_at":"2024-12-03T18:58:19Z","title":"FoundHand: Large-Scale Domain-Specific Learning for Controllable Hand Image Generation","version":2},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-11T23:13:39.449417Z"},"links":{"citing_paper":"/paper/2412.02690"},"observation_digest":"sha256:df7625b7ca0eba03a101f8095b16b4d1f20bdf952ea8fcac8a3a54d6dc986309","observation_id":"afb39c76-e079-4755-95c3-1257bc6d6d45","resolution":{"observed_at":"2026-08-11T23:13:39.449417Z","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":"2026-08-11T23:13:39.453982Z","title":"Hagrid – hand gesture recognition image dataset","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.02690","last_updated":"2024-12-04T20:51:17Z","snapshot_observed_at":"2026-08-12T15:54:02.294599Z","submitted_at":"2024-12-03T18:58:19Z","title":"FoundHand: Large-Scale Domain-Specific Learning for Controllable Hand Image Generation","version":2},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-11T23:13:39.453982Z"},"links":{"citing_paper":"/paper/2412.02690"},"observation_digest":"sha256:34d2950c9a32082acc428d85bd862fd24f30b1894f583fe6824b2e34ca216aa0","observation_id":"56c461c5-d6bd-4774-9555-c3888014d457","resolution":{"observed_at":"2026-08-11T23:13:39.453982Z","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":"2026-08-11T23:13:39.459007Z","title":"A style-based generator architecture for generative adversarial networks","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2412.02690","last_updated":"2024-12-04T20:51:17Z","snapshot_observed_at":"2026-08-12T15:54:02.294599Z","submitted_at":"2024-12-03T18:58:19Z","title":"FoundHand: Large-Scale Domain-Specific Learning for Controllable Hand Image Generation","version":2},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-11T23:13:39.459007Z"},"links":{"citing_paper":"/paper/2412.02690"},"observation_digest":"sha256:d070a03a6f5abc86285f069349481cd08043cb3134d2acb80755c181bd65a532","observation_id":"8c7a27cb-0569-41d9-9e30-e07d4cee47cf","resolution":{"observed_at":"2026-08-11T23:13:39.459007Z","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":"2026-08-11T23:13:39.463013Z","title":"3d gaussian splatting for real-time radiance field rendering","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.02690","last_updated":"2024-12-04T20:51:17Z","snapshot_observed_at":"2026-08-12T15:54:02.294599Z","submitted_at":"2024-12-03T18:58:19Z","title":"FoundHand: Large-Scale Domain-Specific Learning for Controllable Hand Image Generation","version":2},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-11T23:13:39.463013Z"},"links":{"citing_paper":"/paper/2412.02690"},"observation_digest":"sha256:a2b6c59aeb143666ea1d05e497e167d3427b0f08bc0460d25af2d579719bb39b","observation_id":"9b47f4a4-2320-42ae-b506-f387febbff42","resolution":{"observed_at":"2026-08-11T23:13:39.463013Z","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":"2026-08-11T23:13:39.466868Z","title":"Sapiens: Foundation for human vi- sion models","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2412.02690","last_updated":"2024-12-04T20:51:17Z","snapshot_observed_at":"2026-08-12T15:54:02.294599Z","submitted_at":"2024-12-03T18:58:19Z","title":"FoundHand: Large-Scale Domain-Specific Learning for Controllable Hand Image Generation","version":2},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-11T23:13:39.466868Z"},"links":{"citing_paper":"/paper/2412.02690"},"observation_digest":"sha256:eb7b04d76e44d82ead4087a1f8518c78aa67f8b2d4119068ce97e153d0065746","observation_id":"8231373b-c16b-4e1a-a553-0ec509d6b4fe","resolution":{"observed_at":"2026-08-11T23:13:39.466868Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2304.02643","last_updated":"2023-04-05T17:59:46Z","snapshot_observed_at":"2026-08-08T05:14:59.435033Z","submitted_at":"2023-04-05T17:59:46Z","title":"Segment Anything","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2304.02643","snapshot_observed_at":"2026-08-11T23:13:39.470768Z","title":"Segment anything","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.02690","last_updated":"2024-12-04T20:51:17Z","snapshot_observed_at":"2026-08-12T15:54:02.294599Z","submitted_at":"2024-12-03T18:58:19Z","title":"FoundHand: Large-Scale Domain-Specific Learning for Controllable Hand Image Generation","version":2},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-11T23:13:39.470768Z"},"links":{"cited_paper":"/paper/2304.02643","citing_paper":"/paper/2412.02690"},"observation_digest":"sha256:33b610a1b9484ce84cde6f639a0c03d100d43ef7c6eb5914bf1e2206f4dcb0b8","observation_id":"6684b4af-c34c-439c-ac30-5b4fcaf09220","resolution":{"observed_at":"2026-08-11T23:13:39.470768Z","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":"2026-08-11T23:13:39.475073Z","title":"Graspdiffusion: Synthe- sizing realistic whole-body hand-object interaction","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.02690","last_updated":"2024-12-04T20:51:17Z","snapshot_observed_at":"2026-08-12T15:54:02.294599Z","submitted_at":"2024-12-03T18:58:19Z","title":"FoundHand: Large-Scale Domain-Specific Learning for Controllable Hand Image Generation","version":2},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-11T23:13:39.475073Z"},"links":{"citing_paper":"/paper/2412.02690"},"observation_digest":"sha256:b457f27875d8e122c7d9a92ce03bd6f37851cceb7ed826e5135db0ef4b172a4d","observation_id":"182ecff7-4513-498f-9d3a-e0be2f9e44c2","resolution":{"observed_at":"2026-08-11T23:13:39.475073Z","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":"2026-08-11T23:13:39.479138Z","title":"Word-level deep sign language recognition from video: A new large-scale dataset and methods comparison","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2412.02690","last_updated":"2024-12-04T20:51:17Z","snapshot_observed_at":"2026-08-12T15:54:02.294599Z","submitted_at":"2024-12-03T18:58:19Z","title":"FoundHand: Large-Scale Domain-Specific Learning for Controllable Hand Image Generation","version":2},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-11T23:13:39.479138Z"},"links":{"citing_paper":"/paper/2412.02690"},"observation_digest":"sha256:960136b269f3982c09c4d67a52261c2ca391dd95b11add3fb2f017d9ff919f20","observation_id":"56a81235-a279-47d8-9c2d-ff6c3cca11b7","resolution":{"observed_at":"2026-08-11T23:13:39.479138Z","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":"2026-08-11T23:13:39.482978Z","title":"Vihope: Visuotactile in-hand object 6d pose estimation with shape completion.IEEE Robotics and Automation Let- ters, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.02690","last_updated":"2024-12-04T20:51:17Z","snapshot_observed_at":"2026-08-12T15:54:02.294599Z","submitted_at":"2024-12-03T18:58:19Z","title":"FoundHand: Large-Scale Domain-Specific Learning for Controllable Hand Image Generation","version":2},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-11T23:13:39.482978Z"},"links":{"citing_paper":"/paper/2412.02690"},"observation_digest":"sha256:25f776f3b70dc15d1d9b4a906bd0873e79bd7753635dfff0918ff1b66c4aea78","observation_id":"d9790155-8492-47df-96fb-7b1c1431dc52","resolution":{"observed_at":"2026-08-11T23:13:39.482978Z","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":"2026-08-11T23:13:39.487372Z","title":"Renderih: A large-scale synthetic dataset for 3d interacting hand pose estimation","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.02690","last_updated":"2024-12-04T20:51:17Z","snapshot_observed_at":"2026-08-12T15:54:02.294599Z","submitted_at":"2024-12-03T18:58:19Z","title":"FoundHand: Large-Scale Domain-Specific Learning for Controllable Hand Image Generation","version":2},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-11T23:13:39.487372Z"},"links":{"citing_paper":"/paper/2412.02690"},"observation_digest":"sha256:cb3eb8a0b859795801b1593c559bc00114dc0612061777338be70427f2bf1e05","observation_id":"e82b506c-e957-46ec-89b8-2c8e09dd46e2","resolution":{"observed_at":"2026-08-11T23:13:39.487372Z","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":"2026-08-11T23:13:39.492339Z","title":"Mesh graphormer","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.02690","last_updated":"2024-12-04T20:51:17Z","snapshot_observed_at":"2026-08-12T15:54:02.294599Z","submitted_at":"2024-12-03T18:58:19Z","title":"FoundHand: Large-Scale Domain-Specific Learning for Controllable Hand Image Generation","version":2},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-11T23:13:39.492339Z"},"links":{"citing_paper":"/paper/2412.02690"},"observation_digest":"sha256:27efae6be429843bac5967ac9e1ff67e7259aca7f97dc3452a717ca96640cfc7","observation_id":"e53c8945-d330-47d9-b8ed-180aae45e950","resolution":{"observed_at":"2026-08-11T23:13:39.492339Z","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":"2026-08-11T23:13:41.155847Z","title":"Microsoft coco: Common objects in context","venue":null,"work_id":"210b24a5-f4a0-45e0-a4d5-7ece0f8cdad0","year":2014},"citing_paper":{"arxiv_id":"2412.02690","last_updated":"2024-12-04T20:51:17Z","snapshot_observed_at":"2026-08-12T15:54:02.294599Z","submitted_at":"2024-12-03T18:58:19Z","title":"FoundHand: Large-Scale Domain-Specific Learning for Controllable Hand Image Generation","version":2},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-11T23:13:39.496785Z"},"links":{"citing_paper":"/paper/2412.02690"},"observation_digest":"sha256:f5c50eb66145023328fc7656df1214e0066b37e0ff1633fc9c5d890502a7879b","observation_id":"4a6b43a4-c499-4b96-9772-624f20fdb82d","resolution":{"observed_at":"2026-08-11T23:13:41.160617Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T23:13:41.141227Z","title":"Zero-1-to-3: Zero-shot one image to 3d object, 2023","venue":null,"work_id":"1a526887-42ad-4dfa-9bc9-36d02bb2e074","year":2023},"citing_paper":{"arxiv_id":"2412.02690","last_updated":"2024-12-04T20:51:17Z","snapshot_observed_at":"2026-08-12T15:54:02.294599Z","submitted_at":"2024-12-03T18:58:19Z","title":"FoundHand: Large-Scale Domain-Specific Learning for Controllable Hand Image Generation","version":2},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-11T23:13:39.501240Z"},"links":{"citing_paper":"/paper/2412.02690"},"observation_digest":"sha256:c3e993fa6bb8dc6fb5275a70933cc9d8d9ba9a33930650a4c127f25e70ada019","observation_id":"a3609584-18a1-4811-8a97-78b930c7702d","resolution":{"observed_at":"2026-08-11T23:13:41.145898Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T23:13:41.127180Z","title":"Hoi4d: A 4d egocentric dataset for category-level human-object interaction","venue":null,"work_id":"e622e073-0a50-4da0-9eed-7d2aa16f74d0","year":2022},"citing_paper":{"arxiv_id":"2412.02690","last_updated":"2024-12-04T20:51:17Z","snapshot_observed_at":"2026-08-12T15:54:02.294599Z","submitted_at":"2024-12-03T18:58:19Z","title":"FoundHand: Large-Scale Domain-Specific Learning for Controllable Hand Image Generation","version":2},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-11T23:13:39.505807Z"},"links":{"citing_paper":"/paper/2412.02690"},"observation_digest":"sha256:b145d1f7f2a3091aaa2c3725e5105ffe7e83436d3b6ab13b6540752587533a6c","observation_id":"884452b4-4854-4b80-acf8-188aabb274f8","resolution":{"observed_at":"2026-08-11T23:13:41.131583Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T23:13:41.112797Z","title":"Diva-360: The dynamic visual dataset for immersive neu- ral fields","venue":null,"work_id":"38d24577-22cc-4ef9-8694-52f0d529fc37","year":2024},"citing_paper":{"arxiv_id":"2412.02690","last_updated":"2024-12-04T20:51:17Z","snapshot_observed_at":"2026-08-12T15:54:02.294599Z","submitted_at":"2024-12-03T18:58:19Z","title":"FoundHand: Large-Scale Domain-Specific Learning for Controllable Hand Image Generation","version":2},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-11T23:13:39.510390Z"},"links":{"citing_paper":"/paper/2412.02690"},"observation_digest":"sha256:e816423422b68e27a9016b7f7def4961ab1e7ba1446effce0a69d0d2f964af09","observation_id":"d44b2a32-7fcf-42a7-9085-4843bca518a1","resolution":{"observed_at":"2026-08-11T23:13:41.117246Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2311.17957","last_updated":"2024-08-16T05:35:21Z","snapshot_observed_at":"2026-08-13T05:14:43.238194Z","submitted_at":"2023-11-29T08:52:08Z","title":"HandRefiner: Refining Malformed Hands in Generated Images by Diffusion-based Conditional Inpainting","version":2},"cited_work":{"arxiv_id":"2311.17957","doi":null,"metadata_source":"pith","pith_arxiv_id":"2311.17957","snapshot_observed_at":"2026-08-11T23:13:40.211888Z","title":"HandRefiner: Refining Malformed Hands in Generated Images by Diffusion-based Conditional Inpainting","venue":"cs.CV","work_id":"d7a583ec-0173-4a21-ae37-c7c609dac835","year":2023},"citing_paper":{"arxiv_id":"2412.02690","last_updated":"2024-12-04T20:51:17Z","snapshot_observed_at":"2026-08-12T15:54:02.294599Z","submitted_at":"2024-12-03T18:58:19Z","title":"FoundHand: Large-Scale Domain-Specific Learning for Controllable Hand Image Generation","version":2},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-11T23:13:39.514753Z"},"links":{"cited_paper":"/paper/2311.17957","citing_paper":"/paper/2412.02690"},"observation_digest":"sha256:9c778f20bfe8f3be18884c757ea744071430f3bcaaac0bbe8a36419a14da2cab","observation_id":"1f463a6d-2225-4293-8ce4-039596597a59","resolution":{"observed_at":"2026-08-11T23:13:40.216818Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1906.08172","last_updated":"2019-06-14T05:49:22Z","snapshot_observed_at":"2026-08-05T19:15:35.517082Z","submitted_at":"2019-06-14T05:49:22Z","title":"MediaPipe: A Framework for Building Perception Pipelines","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1906.08172","snapshot_observed_at":"2026-08-11T23:13:39.519592Z","title":"Me- diapipe: A framework for building perception pipelines","venue":null,"work_id":null,"year":1906},"citing_paper":{"arxiv_id":"2412.02690","last_updated":"2024-12-04T20:51:17Z","snapshot_observed_at":"2026-08-12T15:54:02.294599Z","submitted_at":"2024-12-03T18:58:19Z","title":"FoundHand: Large-Scale Domain-Specific Learning for Controllable Hand Image Generation","version":2},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-11T23:13:39.519592Z"},"links":{"cited_paper":"/paper/1906.08172","citing_paper":"/paper/2412.02690"},"observation_digest":"sha256:50354fcc21ebe4ee05087c3ac49ce11bbf525ca0e804b1ffec9b1bb821b8ee2d","observation_id":"04fae287-2ef3-4331-b68c-9dd5ef7a86a3","resolution":{"observed_at":"2026-08-11T23:13:39.519592Z","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":"2026-08-11T23:13:39.524357Z","title":"Repaint: Inpainting using denoising diffusion probabilistic models,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.02690","last_updated":"2024-12-04T20:51:17Z","snapshot_observed_at":"2026-08-12T15:54:02.294599Z","submitted_at":"2024-12-03T18:58:19Z","title":"FoundHand: Large-Scale Domain-Specific Learning for Controllable Hand Image Generation","version":2},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-11T23:13:39.524357Z"},"links":{"citing_paper":"/paper/2412.02690"},"observation_digest":"sha256:7b235e0d3ec153e59ba910e6f280362616ee6aa52361e33440305738abbc8338","observation_id":"73b98e06-5833-4348-b74b-2f2e7ce74f95","resolution":{"observed_at":"2026-08-11T23:13:39.524357Z","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":"2026-08-11T23:13:41.090164Z","title":"Pose guided person image gener- ation","venue":null,"work_id":"dd5cfa51-1456-4d0e-a69b-f2838bb523df","year":2017},"citing_paper":{"arxiv_id":"2412.02690","last_updated":"2024-12-04T20:51:17Z","snapshot_observed_at":"2026-08-12T15:54:02.294599Z","submitted_at":"2024-12-03T18:58:19Z","title":"FoundHand: Large-Scale Domain-Specific Learning for Controllable Hand Image Generation","version":2},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-11T23:13:39.529530Z"},"links":{"citing_paper":"/paper/2412.02690"},"observation_digest":"sha256:6bbd1cc91468c038d708a13731644ef27efe52f9271d3899c46f30e19fddc228","observation_id":"737cfe36-dc90-4747-bf56-e0b2870cdc81","resolution":{"observed_at":"2026-08-11T23:13:41.094519Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T23:13:41.075752Z","title":"Nerf: Representing scenes as neural radiance fields for view syn- thesis","venue":null,"work_id":"3b4ed30f-1667-4dd0-a65d-d652c4ac4432","year":2021},"citing_paper":{"arxiv_id":"2412.02690","last_updated":"2024-12-04T20:51:17Z","snapshot_observed_at":"2026-08-12T15:54:02.294599Z","submitted_at":"2024-12-03T18:58:19Z","title":"FoundHand: Large-Scale Domain-Specific Learning for Controllable Hand Image Generation","version":2},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-11T23:13:39.533969Z"},"links":{"citing_paper":"/paper/2412.02690"},"observation_digest":"sha256:4dbaacb3fcb6171ebc854ed34e7df93659911d1fb22d3ea914cef746492e10fe","observation_id":"0d4fce3b-4db5-489c-9eae-7cabb623f476","resolution":{"observed_at":"2026-08-11T23:13:41.080455Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.05059","last_updated":"2024-06-15T03:10:59Z","snapshot_observed_at":"2026-08-13T03:18:39.281558Z","submitted_at":"2024-06-07T16:31:41Z","title":"GenHeld: Generating and Editing Handheld Objects","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.05059","snapshot_observed_at":"2026-08-11T23:13:39.538532Z","title":"Genheld: Generating and editing handheld objects","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.02690","last_updated":"2024-12-04T20:51:17Z","snapshot_observed_at":"2026-08-12T15:54:02.294599Z","submitted_at":"2024-12-03T18:58:19Z","title":"FoundHand: Large-Scale Domain-Specific Learning for Controllable Hand Image Generation","version":2},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-11T23:13:39.538532Z"},"links":{"cited_paper":"/paper/2406.05059","citing_paper":"/paper/2412.02690"},"observation_digest":"sha256:64e2d03c16f37e443d1574182aafcb5b62f256374d9011faebb0c33cc708cb89","observation_id":"2be8fe8d-d61e-46e4-a046-95c9c9f7a43c","resolution":{"observed_at":"2026-08-11T23:13:39.538532Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2311.17878","last_updated":"2023-11-29T18:23:18Z","snapshot_observed_at":"2026-08-13T05:14:12.652118Z","submitted_at":"2023-11-29T18:23:18Z","title":"TSDF-Sampling: Efficient Sampling for Neural Surface Field using Truncated Signed Distance Field","version":1},"cited_work":{"arxiv_id":"2311.17878","doi":null,"metadata_source":"pith","pith_arxiv_id":"2311.17878","snapshot_observed_at":"2026-08-11T23:13:40.159183Z","title":"TSDF-Sampling: Efficient Sampling for Neural Surface Field using Truncated Signed Distance Field","venue":"cs.CV","work_id":"13db49ba-620e-4aab-8819-b658bbb0aed3","year":2023},"citing_paper":{"arxiv_id":"2412.02690","last_updated":"2024-12-04T20:51:17Z","snapshot_observed_at":"2026-08-12T15:54:02.294599Z","submitted_at":"2024-12-03T18:58:19Z","title":"FoundHand: Large-Scale Domain-Specific Learning for Controllable Hand Image Generation","version":2},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-11T23:13:39.543290Z"},"links":{"cited_paper":"/paper/2311.17878","citing_paper":"/paper/2412.02690"},"observation_digest":"sha256:7930c95225aeb8af41c35f193f9935205c88f7f8ba3c8e5a532eace6072cb23b","observation_id":"d2d8f302-320b-40ff-bebe-ab6544586cd8","resolution":{"observed_at":"2026-08-11T23:13:40.164473Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T23:13:41.061542Z","title":"Interhand2","venue":null,"work_id":"91951e4d-f511-4433-9a42-0416b73fd176","year":2020},"citing_paper":{"arxiv_id":"2412.02690","last_updated":"2024-12-04T20:51:17Z","snapshot_observed_at":"2026-08-12T15:54:02.294599Z","submitted_at":"2024-12-03T18:58:19Z","title":"FoundHand: Large-Scale Domain-Specific Learning for Controllable Hand Image Generation","version":2},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-11T23:13:39.547768Z"},"links":{"citing_paper":"/paper/2412.02690"},"observation_digest":"sha256:a69c55db13e6a0f7c8265a96d91f1c5cda33c6e755e4aadb35ca0982fb50d6ae","observation_id":"dec68964-3d84-44f8-ba29-da5445f9dffa","resolution":{"observed_at":"2026-08-11T23:13:41.066131Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T23:13:41.046824Z","title":"A dataset of relighted 3D interacting hands","venue":null,"work_id":"8d996f3e-bc56-40b0-9d0c-33ebf3ffdbd7","year":2023},"citing_paper":{"arxiv_id":"2412.02690","last_updated":"2024-12-04T20:51:17Z","snapshot_observed_at":"2026-08-12T15:54:02.294599Z","submitted_at":"2024-12-03T18:58:19Z","title":"FoundHand: Large-Scale Domain-Specific Learning for Controllable Hand Image Generation","version":2},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-11T23:13:39.552012Z"},"links":{"citing_paper":"/paper/2412.02690"},"observation_digest":"sha256:66daad570e211b45bcdf5cfafc69621564ba58864995702507cb4118065f2178","observation_id":"5e9d0dcd-ccf5-49b1-997d-c953ac75c500","resolution":{"observed_at":"2026-08-11T23:13:41.051486Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T23:13:39.556379Z","title":"Instant neural graphics primitives with a multiresolution hash encoding","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.02690","last_updated":"2024-12-04T20:51:17Z","snapshot_observed_at":"2026-08-12T15:54:02.294599Z","submitted_at":"2024-12-03T18:58:19Z","title":"FoundHand: Large-Scale Domain-Specific Learning for Controllable Hand Image Generation","version":2},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-11T23:13:39.556379Z"},"links":{"citing_paper":"/paper/2412.02690"},"observation_digest":"sha256:254b9a9437358c7a10913fbf2152a5a47b670d2587a0940796d391ac9ddc1677","observation_id":"6fcf6795-1634-4660-9b00-aa2a9f869d12","resolution":{"observed_at":"2026-08-11T23:13:39.556379Z","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":"2026-08-11T23:13:41.022283Z","title":"Han- diffuser: Text-to-image generation with realistic hand ap- pearances","venue":null,"work_id":"ecfcfc29-0a70-4880-9fed-02864e778d6a","year":2024},"citing_paper":{"arxiv_id":"2412.02690","last_updated":"2024-12-04T20:51:17Z","snapshot_observed_at":"2026-08-12T15:54:02.294599Z","submitted_at":"2024-12-03T18:58:19Z","title":"FoundHand: Large-Scale Domain-Specific Learning for Controllable Hand Image Generation","version":2},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-11T23:13:39.560682Z"},"links":{"citing_paper":"/paper/2412.02690"},"observation_digest":"sha256:022965bb6fa1f5acb674631633cede50c2fd11dbbd2b62df356fc7c5a163b8dc","observation_id":"aaf7706f-37fb-4f10-bfc1-9ac0759781cb","resolution":{"observed_at":"2026-08-11T23:13:41.026935Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T23:13:41.006422Z","title":"Han- diffuser: Text-to-image generation with realistic hand ap- pearances","venue":null,"work_id":"664b32c0-c543-483f-8e2a-5673d0016580","year":2024},"citing_paper":{"arxiv_id":"2412.02690","last_updated":"2024-12-04T20:51:17Z","snapshot_observed_at":"2026-08-12T15:54:02.294599Z","submitted_at":"2024-12-03T18:58:19Z","title":"FoundHand: Large-Scale Domain-Specific Learning for Controllable Hand Image Generation","version":2},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-11T23:13:39.565771Z"},"links":{"citing_paper":"/paper/2412.02690"},"observation_digest":"sha256:7213340aabc41532aaba9f3555f08808e2f71b56dfc22bdb26abcb77bdea4c79","observation_id":"b6f62173-7fa0-44a2-96f0-12a894f20448","resolution":{"observed_at":"2026-08-11T23:13:41.011803Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T23:13:40.991393Z","title":"Im- proved denoising diffusion probabilistic models","venue":null,"work_id":"fac59aee-a26b-4719-9cf0-39379ed0228c","year":null},"citing_paper":{"arxiv_id":"2412.02690","last_updated":"2024-12-04T20:51:17Z","snapshot_observed_at":"2026-08-12T15:54:02.294599Z","submitted_at":"2024-12-03T18:58:19Z","title":"FoundHand: Large-Scale Domain-Specific Learning for Controllable Hand Image Generation","version":2},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-11T23:13:39.570263Z"},"links":{"citing_paper":"/paper/2412.02690"},"observation_digest":"sha256:90e472c449be5b4b0da06247bbb34a2585395fb9e576e214505de48219c45792","observation_id":"285ead25-7c19-47ba-80c2-d596d56130a9","resolution":{"observed_at":"2026-08-11T23:13:40.996311Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T23:13:40.976357Z","title":"Fsgan: Subject agnostic face swapping and reenactment","venue":null,"work_id":"dd84cf8a-09e5-4d46-8589-6da45d88983e","year":2019},"citing_paper":{"arxiv_id":"2412.02690","last_updated":"2024-12-04T20:51:17Z","snapshot_observed_at":"2026-08-12T15:54:02.294599Z","submitted_at":"2024-12-03T18:58:19Z","title":"FoundHand: Large-Scale Domain-Specific Learning for Controllable Hand Image Generation","version":2},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-11T23:13:39.575204Z"},"links":{"citing_paper":"/paper/2412.02690"},"observation_digest":"sha256:77632d6efa73fcc48ec2999f3709f71c34f821d6a2324d380d4b1ad61836aaeb","observation_id":"855e82a8-1a1e-490c-8a4d-b47ee2eced3c","resolution":{"observed_at":"2026-08-11T23:13:40.981293Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T23:13:40.960865Z","title":"Assemblyhands: Towards egocentric activity understanding via 3d hand pose estima- tion","venue":null,"work_id":"233cc07e-29d7-492a-86f0-180771f3a905","year":null},"citing_paper":{"arxiv_id":"2412.02690","last_updated":"2024-12-04T20:51:17Z","snapshot_observed_at":"2026-08-12T15:54:02.294599Z","submitted_at":"2024-12-03T18:58:19Z","title":"FoundHand: Large-Scale Domain-Specific Learning for Controllable Hand Image Generation","version":2},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-11T23:13:39.579662Z"},"links":{"citing_paper":"/paper/2412.02690"},"observation_digest":"sha256:92109737c4349fee2016ca03ef6c0d50d74bdcff2b74cb373c8147c7d1b8d089","observation_id":"804582a4-fb67-44b3-b1c2-cd47c6665e6f","resolution":{"observed_at":"2026-08-11T23:13:40.965717Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T23:13:40.945288Z","title":"AssemblyHands: towards egocentric activity understanding via 3d hand pose esti- mation","venue":null,"work_id":"bab4f3fc-1349-41a7-9758-9b3c86179e2e","year":2023},"citing_paper":{"arxiv_id":"2412.02690","last_updated":"2024-12-04T20:51:17Z","snapshot_observed_at":"2026-08-12T15:54:02.294599Z","submitted_at":"2024-12-03T18:58:19Z","title":"FoundHand: Large-Scale Domain-Specific Learning for Controllable Hand Image Generation","version":2},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-11T23:13:39.584253Z"},"links":{"citing_paper":"/paper/2412.02690"},"observation_digest":"sha256:643a71a3f26aa4b20b1418b498b002afaf71f3b0f4d9b348f9af35da4ceda7f2","observation_id":"3ae47099-1047-4944-9fe4-7642c600d065","resolution":{"observed_at":"2026-08-11T23:13:40.949958Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T23:13:40.930858Z","title":null,"venue":null,"work_id":"9ed15bfd-a2dc-4ec0-9e5a-06138d6ef9a0","year":2023},"citing_paper":{"arxiv_id":"2412.02690","last_updated":"2024-12-04T20:51:17Z","snapshot_observed_at":"2026-08-12T15:54:02.294599Z","submitted_at":"2024-12-03T18:58:19Z","title":"FoundHand: Large-Scale Domain-Specific Learning for Controllable Hand Image Generation","version":2},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-11T23:13:39.588612Z"},"links":{"citing_paper":"/paper/2412.02690"},"observation_digest":"sha256:ae7be0ec4e85935bb0d44cb19b22e2fbd027a5620d8ef295dc0ebdfda4190b5e","observation_id":"711a706e-c010-48f5-8650-4dc14b2a7cbc","resolution":{"observed_at":"2026-08-11T23:13:40.935098Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2407.18034","last_updated":"2024-07-25T13:29:32Z","snapshot_observed_at":"2026-08-13T08:47:12.790419Z","submitted_at":"2024-07-25T13:29:32Z","title":"AttentionHand: Text-driven Controllable Hand Image Generation for 3D Hand Reconstruction in the Wild","version":1},"cited_work":{"arxiv_id":"2407.18034","doi":null,"metadata_source":"pith","pith_arxiv_id":"2407.18034","snapshot_observed_at":"2026-08-11T23:13:40.137977Z","title":"AttentionHand: Text-driven Controllable Hand Image Generation for 3D Hand Reconstruction in the Wild","venue":"cs.CV","work_id":"b7fc9673-4fff-4ee0-8e37-aae29a60170a","year":2024},"citing_paper":{"arxiv_id":"2412.02690","last_updated":"2024-12-04T20:51:17Z","snapshot_observed_at":"2026-08-12T15:54:02.294599Z","submitted_at":"2024-12-03T18:58:19Z","title":"FoundHand: Large-Scale Domain-Specific Learning for Controllable Hand Image Generation","version":2},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-11T23:13:39.592467Z"},"links":{"cited_paper":"/paper/2407.18034","citing_paper":"/paper/2412.02690"},"observation_digest":"sha256:af886c0f2e2bb886bf113ee6f11ca0a4701ad3f908acbed676d34ad9d01b2864","observation_id":"e37a568b-5c4f-4a3b-867d-ab2154205ce5","resolution":{"observed_at":"2026-08-11T23:13:40.142684Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T23:13:40.917588Z","title":"Re- constructing hands in 3D with transformers","venue":null,"work_id":"f94495f1-9826-4b2c-9d61-e718434868eb","year":null},"citing_paper":{"arxiv_id":"2412.02690","last_updated":"2024-12-04T20:51:17Z","snapshot_observed_at":"2026-08-12T15:54:02.294599Z","submitted_at":"2024-12-03T18:58:19Z","title":"FoundHand: Large-Scale Domain-Specific Learning for Controllable Hand Image Generation","version":2},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-11T23:13:39.596588Z"},"links":{"citing_paper":"/paper/2412.02690"},"observation_digest":"sha256:23e612c1ae878fa57147842d7fe959094e2da90a934b8c215f7866243442bdee","observation_id":"06a77d6b-cb76-442e-b448-5d6873963ce3","resolution":{"observed_at":"2026-08-11T23:13:40.921828Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2212.09748","last_updated":"2023-03-02T09:06:55Z","snapshot_observed_at":"2026-07-06T14:32:37.317828Z","submitted_at":"2022-12-19T18:59:58Z","title":"Scalable Diffusion Models with Transformers","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2212.09748","snapshot_observed_at":"2026-08-11T23:13:39.601219Z","title":"Scalable diffusion mod- els with transformers","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.02690","last_updated":"2024-12-04T20:51:17Z","snapshot_observed_at":"2026-08-12T15:54:02.294599Z","submitted_at":"2024-12-03T18:58:19Z","title":"FoundHand: Large-Scale Domain-Specific Learning for Controllable Hand Image Generation","version":2},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-08-11T23:13:39.601219Z"},"links":{"cited_paper":"/paper/2212.09748","citing_paper":"/paper/2412.02690"},"observation_digest":"sha256:17a4c7d5f9d6d9ac31adcc8cca817303edde0f6de65bb6d96a7a0e1e4f07eee2","observation_id":"d690d18b-b676-476d-a465-1a87cf5be3eb","resolution":{"observed_at":"2026-08-11T23:13:39.601219Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2408.06070","last_updated":"2025-03-08T08:43:03Z","snapshot_observed_at":"2026-08-12T23:05:02.930655Z","submitted_at":"2024-08-12T11:41:18Z","title":"ControlNeXt: Powerful and Efficient Control for Image and Video Generation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.06070","snapshot_observed_at":"2026-08-11T23:13:39.606288Z","title":"Controlnext: Powerful and effi- cient control for image and video generation.arXiv preprint arXiv:2408.06070, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.02690","last_updated":"2024-12-04T20:51:17Z","snapshot_observed_at":"2026-08-12T15:54:02.294599Z","submitted_at":"2024-12-03T18:58:19Z","title":"FoundHand: Large-Scale Domain-Specific Learning for Controllable Hand Image Generation","version":2},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-08-11T23:13:39.606288Z"},"links":{"cited_paper":"/paper/2408.06070","citing_paper":"/paper/2412.02690"},"observation_digest":"sha256:f3033ca7b5b55cd527db9ec508bdaa8118d85c5306aa61436ce913d97d54f326","observation_id":"504ba44e-8a50-41ef-b2d3-23641047e9aa","resolution":{"observed_at":"2026-08-11T23:13:39.606288Z","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":"2026-08-11T23:13:40.903302Z","title":"Manus: Markerless grasp capture using articulated 3d gaussians","venue":null,"work_id":"4097c04b-15bb-4b9a-bcc3-fae7eaf4b992","year":2024},"citing_paper":{"arxiv_id":"2412.02690","last_updated":"2024-12-04T20:51:17Z","snapshot_observed_at":"2026-08-12T15:54:02.294599Z","submitted_at":"2024-12-03T18:58:19Z","title":"FoundHand: Large-Scale Domain-Specific Learning for Controllable Hand Image Generation","version":2},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-08-11T23:13:39.610970Z"},"links":{"citing_paper":"/paper/2412.02690"},"observation_digest":"sha256:f87abefa4972790d5f755b8c911b2ef25eb24658a264f15ae6142bc53119bf99","observation_id":"0886d81b-7e34-4086-9a11-1e769b0f7724","resolution":{"observed_at":"2026-08-11T23:13:40.908049Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2410.13720","last_updated":"2025-02-26T16:05:55Z","snapshot_observed_at":"2026-08-13T11:35:08.107504Z","submitted_at":"2024-10-17T16:22:46Z","title":"Movie Gen: A Cast of Media Foundation Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.13720","snapshot_observed_at":"2026-08-11T23:13:39.615983Z","title":"Movie gen: A cast of media foundation models","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.02690","last_updated":"2024-12-04T20:51:17Z","snapshot_observed_at":"2026-08-12T15:54:02.294599Z","submitted_at":"2024-12-03T18:58:19Z","title":"FoundHand: Large-Scale Domain-Specific Learning for Controllable Hand Image Generation","version":2},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-08-11T23:13:39.615983Z"},"links":{"cited_paper":"/paper/2410.13720","citing_paper":"/paper/2412.02690"},"observation_digest":"sha256:e2fe3a94e6b8f0cb45a3009b8ceccd59b89e3cbc2c23ff434179efc38366f3ee","observation_id":"1297eee8-a258-4d0a-9d18-080f317ec201","resolution":{"observed_at":"2026-08-11T23:13:39.615983Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2209.14988","last_updated":"2022-09-29T17:50:40Z","snapshot_observed_at":"2026-07-06T13:57:54.539656Z","submitted_at":"2022-09-29T17:50:40Z","title":"DreamFusion: Text-to-3D using 2D Diffusion","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2209.14988","snapshot_observed_at":"2026-08-11T23:13:39.620891Z","title":"Dreamfusion: Text-to-3d using 2d diffusion","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.02690","last_updated":"2024-12-04T20:51:17Z","snapshot_observed_at":"2026-08-12T15:54:02.294599Z","submitted_at":"2024-12-03T18:58:19Z","title":"FoundHand: Large-Scale Domain-Specific Learning for Controllable Hand Image Generation","version":2},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-08-11T23:13:39.620891Z"},"links":{"cited_paper":"/paper/2209.14988","citing_paper":"/paper/2412.02690"},"observation_digest":"sha256:2dcd386d31463ee444f9cebbe51a287b4c1041f6370bae3a4d718a2a2461300b","observation_id":"13bf10e4-8876-425c-a6fb-d4816704255b","resolution":{"observed_at":"2026-08-11T23:13:39.620891Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2305.11147","last_updated":"2023-11-02T17:59:06Z","snapshot_observed_at":"2026-08-13T11:39:17.693095Z","submitted_at":"2023-05-18T17:41:34Z","title":"UniControl: A Unified Diffusion Model for Controllable Visual Generation In the Wild","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.11147","snapshot_observed_at":"2026-08-11T23:13:39.625611Z","title":"Unicontrol: A unified diffu- sion model for controllable visual generation in the wild","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.02690","last_updated":"2024-12-04T20:51:17Z","snapshot_observed_at":"2026-08-12T15:54:02.294599Z","submitted_at":"2024-12-03T18:58:19Z","title":"FoundHand: Large-Scale Domain-Specific Learning for Controllable Hand Image Generation","version":2},"reference_index":71,"source":"pdf_text","source_observed_at":"2026-08-11T23:13:39.625611Z"},"links":{"cited_paper":"/paper/2305.11147","citing_paper":"/paper/2412.02690"},"observation_digest":"sha256:51fcd0f462faa82de53a6dce2989e9a9d7b3f9fe6d0558a780c8c1c69ec6fd88","observation_id":"b274557a-20d1-446f-acb9-e36edd43d8b3","resolution":{"observed_at":"2026-08-11T23:13:39.625611Z","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":"2026-08-11T23:13:40.888671Z","title":"Handcraft: Anatomically correct restoration of malformed hands in diffusion generated images","venue":null,"work_id":"fd22aa53-b009-4ba8-93bb-651bc24e21c6","year":2024},"citing_paper":{"arxiv_id":"2412.02690","last_updated":"2024-12-04T20:51:17Z","snapshot_observed_at":"2026-08-12T15:54:02.294599Z","submitted_at":"2024-12-03T18:58:19Z","title":"FoundHand: Large-Scale Domain-Specific Learning for Controllable Hand Image Generation","version":2},"reference_index":72,"source":"pdf_text","source_observed_at":"2026-08-11T23:13:39.630653Z"},"links":{"citing_paper":"/paper/2412.02690"},"observation_digest":"sha256:bbabc05c8c359b14869604bec5d0893895e19bd764f39b0ea75e3ed3d55ac955","observation_id":"b5536b0e-e5a3-4a53-a8c0-5c60130a8789","resolution":{"observed_at":"2026-08-11T23:13:40.893433Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T23:13:39.635369Z","title":"Learn- ing transferable visual models from natural language super- vision","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.02690","last_updated":"2024-12-04T20:51:17Z","snapshot_observed_at":"2026-08-12T15:54:02.294599Z","submitted_at":"2024-12-03T18:58:19Z","title":"FoundHand: Large-Scale Domain-Specific Learning for Controllable Hand Image Generation","version":2},"reference_index":73,"source":"pdf_text","source_observed_at":"2026-08-11T23:13:39.635369Z"},"links":{"citing_paper":"/paper/2412.02690"},"observation_digest":"sha256:c2ae350e1a768e44c2fa51be68bc00a3759f0a15f234feec949a71830e3798bc","observation_id":"99fb7e86-e6a0-47e0-b9a0-0b128a6fe51b","resolution":{"observed_at":"2026-08-11T23:13:39.635369Z","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":"2026-08-11T23:13:40.863778Z","title":"Towards realistic generative 3d face models","venue":null,"work_id":"00464871-6e40-4a6d-b711-9b38c3ecd2cd","year":2024},"citing_paper":{"arxiv_id":"2412.02690","last_updated":"2024-12-04T20:51:17Z","snapshot_observed_at":"2026-08-12T15:54:02.294599Z","submitted_at":"2024-12-03T18:58:19Z","title":"FoundHand: Large-Scale Domain-Specific Learning for Controllable Hand Image Generation","version":2},"reference_index":74,"source":"pdf_text","source_observed_at":"2026-08-11T23:13:39.639860Z"},"links":{"citing_paper":"/paper/2412.02690"},"observation_digest":"sha256:3736b92708338fb93a67b33ea24f0ca12e9f27e60aaf9cefc6ca8d10b8c35b5d","observation_id":"903eb1f3-338d-4cb0-ba2f-05162cd72e1e","resolution":{"observed_at":"2026-08-11T23:13:40.869052Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T23:13:40.848067Z","title":"Zero-shot text-to-image generation","venue":null,"work_id":"d7fdf9c6-8169-42d0-8dba-0f5f3576fe49","year":2021},"citing_paper":{"arxiv_id":"2412.02690","last_updated":"2024-12-04T20:51:17Z","snapshot_observed_at":"2026-08-12T15:54:02.294599Z","submitted_at":"2024-12-03T18:58:19Z","title":"FoundHand: Large-Scale Domain-Specific Learning for Controllable Hand Image Generation","version":2},"reference_index":76,"source":"pdf_text","source_observed_at":"2026-08-11T23:13:39.648599Z"},"links":{"citing_paper":"/paper/2412.02690"},"observation_digest":"sha256:a7af3a210bf8ce9892c1b97fe7247211e84ec8122e45c09e4c0d75f39ff28ae8","observation_id":"f159a9a8-61f7-40c1-9b73-e2cc3e0af7bb","resolution":{"observed_at":"2026-08-11T23:13:40.853208Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2204.06125","last_updated":"2022-04-13T01:10:33Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2022-04-13T01:10:33Z","title":"Hierarchical Text-Conditional Image Generation with CLIP Latents","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2204.06125","snapshot_observed_at":"2026-08-11T23:13:39.653005Z","title":"Hierarchical text-conditional image generation with clip latents","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.02690","last_updated":"2024-12-04T20:51:17Z","snapshot_observed_at":"2026-08-12T15:54:02.294599Z","submitted_at":"2024-12-03T18:58:19Z","title":"FoundHand: Large-Scale Domain-Specific Learning for Controllable Hand Image Generation","version":2},"reference_index":77,"source":"pdf_text","source_observed_at":"2026-08-11T23:13:39.653005Z"},"links":{"cited_paper":"/paper/2204.06125","citing_paper":"/paper/2412.02690"},"observation_digest":"sha256:bd569d327ed407c08ccaf4917a129d6db1a1bf10f2a15f079e39c0dde97059b5","observation_id":"d4ebb817-cbe5-45bc-b2d8-eaa5e10e5630","resolution":{"observed_at":"2026-08-11T23:13:39.653005Z","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":"2026-08-11T23:13:40.833993Z","title":"Li, and Shan Liu","venue":null,"work_id":"bcd01462-40c3-4771-b0d5-a1af3e16874d","year":2021},"citing_paper":{"arxiv_id":"2412.02690","last_updated":"2024-12-04T20:51:17Z","snapshot_observed_at":"2026-08-12T15:54:02.294599Z","submitted_at":"2024-12-03T18:58:19Z","title":"FoundHand: Large-Scale Domain-Specific Learning for Controllable Hand Image Generation","version":2},"reference_index":78,"source":"pdf_text","source_observed_at":"2026-08-11T23:13:39.658564Z"},"links":{"citing_paper":"/paper/2412.02690"},"observation_digest":"sha256:e651419bcfd29a5238aebfec83be8b9148fdbfa9658cfd61e719f99d7442e3c9","observation_id":"f88909dd-6255-49d9-be80-4d5ac3a37934","resolution":{"observed_at":"2026-08-11T23:13:40.838555Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T23:13:40.819529Z","title":"High-resolution image synthesis with latent diffusion models, 2021","venue":null,"work_id":"c71af915-f377-41ed-9f3c-52955ea4888d","year":2021},"citing_paper":{"arxiv_id":"2412.02690","last_updated":"2024-12-04T20:51:17Z","snapshot_observed_at":"2026-08-12T15:54:02.294599Z","submitted_at":"2024-12-03T18:58:19Z","title":"FoundHand: Large-Scale Domain-Specific Learning for Controllable Hand Image Generation","version":2},"reference_index":80,"source":"pdf_text","source_observed_at":"2026-08-11T23:13:39.667606Z"},"links":{"citing_paper":"/paper/2412.02690"},"observation_digest":"sha256:ea6a15867da4570154fb086cb19ccd6892f44790783190bba6459f67083e78e4","observation_id":"54a7b607-1b87-4579-b6ea-e5995c8be928","resolution":{"observed_at":"2026-08-11T23:13:40.824095Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2201.02610","last_updated":"2022-01-07T18:59:32Z","snapshot_observed_at":"2026-08-09T08:53:11.112938Z","submitted_at":"2022-01-07T18:59:32Z","title":"Embodied Hands: Modeling and Capturing Hands and Bodies Together","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2201.02610","snapshot_observed_at":"2026-08-11T23:13:39.672613Z","title":"Embodied hands: Modeling and capturing hands and bod- ies together","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.02690","last_updated":"2024-12-04T20:51:17Z","snapshot_observed_at":"2026-08-12T15:54:02.294599Z","submitted_at":"2024-12-03T18:58:19Z","title":"FoundHand: Large-Scale Domain-Specific Learning for Controllable Hand Image Generation","version":2},"reference_index":81,"source":"pdf_text","source_observed_at":"2026-08-11T23:13:39.672613Z"},"links":{"cited_paper":"/paper/2201.02610","citing_paper":"/paper/2412.02690"},"observation_digest":"sha256:3078f01730fd77f5b4ecbe9e78155550a7b48e1b086fd106520ba7c7c5238ac0","observation_id":"9be73f9b-79eb-4359-9c00-5f373080e873","resolution":{"observed_at":"2026-08-11T23:13:39.672613Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2205.11487","last_updated":"2022-05-23T17:42:53Z","snapshot_observed_at":"2026-08-12T12:48:35.419134Z","submitted_at":"2022-05-23T17:42:53Z","title":"Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2205.11487","snapshot_observed_at":"2026-08-11T23:13:39.677799Z","title":"Denton, Seyed Kamyar Seyed Ghasemipour, Burcu Karagol Ayan, Seyedeh Sara Mah- davi, Raphael Gontijo Lopes, Tim Salimans, Jonathan Ho, David J","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.02690","last_updated":"2024-12-04T20:51:17Z","snapshot_observed_at":"2026-08-12T15:54:02.294599Z","submitted_at":"2024-12-03T18:58:19Z","title":"FoundHand: Large-Scale Domain-Specific Learning for Controllable Hand Image Generation","version":2},"reference_index":82,"source":"pdf_text","source_observed_at":"2026-08-11T23:13:39.677799Z"},"links":{"cited_paper":"/paper/2205.11487","citing_paper":"/paper/2412.02690"},"observation_digest":"sha256:72d0958644401841ceffe12a84dfa438e3bdf5b92018f1b125ce797b968df864","observation_id":"4b270ee9-744c-4581-ae57-9a745c8901db","resolution":{"observed_at":"2026-08-11T23:13:39.677799Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.14403","last_updated":"2025-01-02T18:59:09Z","snapshot_observed_at":"2026-08-13T00:24:26.491156Z","submitted_at":"2024-04-22T17:58:36Z","title":"GeoDiffuser: Geometry-Based Image Editing with Diffusion Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.14403","snapshot_observed_at":"2026-08-11T23:13:39.683089Z","title":"Geodiffuser: Geometry-based im- age editing with diffusion models","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.02690","last_updated":"2024-12-04T20:51:17Z","snapshot_observed_at":"2026-08-12T15:54:02.294599Z","submitted_at":"2024-12-03T18:58:19Z","title":"FoundHand: Large-Scale Domain-Specific Learning for Controllable Hand Image Generation","version":2},"reference_index":83,"source":"pdf_text","source_observed_at":"2026-08-11T23:13:39.683089Z"},"links":{"cited_paper":"/paper/2404.14403","citing_paper":"/paper/2412.02690"},"observation_digest":"sha256:d6a02a8da86954bcb3a0d3c0a7a223f52875148dbee2e2a13f1944fc529a0472","observation_id":"4d9ef023-879f-4c49-ad3f-f36f8f462fba","resolution":{"observed_at":"2026-08-11T23:13:39.683089Z","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":"2026-08-11T23:13:40.805528Z","title":"Zeronvs: Zero-shot 360-degree view synthesis from a single image","venue":null,"work_id":"a21afc66-46b3-4cfc-990d-75f5de1fffcc","year":2024},"citing_paper":{"arxiv_id":"2412.02690","last_updated":"2024-12-04T20:51:17Z","snapshot_observed_at":"2026-08-12T15:54:02.294599Z","submitted_at":"2024-12-03T18:58:19Z","title":"FoundHand: Large-Scale Domain-Specific Learning for Controllable Hand Image Generation","version":2},"reference_index":84,"source":"pdf_text","source_observed_at":"2026-08-11T23:13:39.688004Z"},"links":{"citing_paper":"/paper/2412.02690"},"observation_digest":"sha256:5ffbd30b6738acbd23c2d1c1ba00f4855ca383a2b90a0b20874b37ea845d669f","observation_id":"822d904b-67e4-4f4b-a752-da1bd6ab77cc","resolution":{"observed_at":"2026-08-11T23:13:40.810095Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T23:13:40.792082Z","title":"Laion-5b: An open large-scale dataset for train- ing next generation image-text models","venue":null,"work_id":"62a38a83-616b-4f05-a4a5-6ac1f4685ce2","year":2022},"citing_paper":{"arxiv_id":"2412.02690","last_updated":"2024-12-04T20:51:17Z","snapshot_observed_at":"2026-08-12T15:54:02.294599Z","submitted_at":"2024-12-03T18:58:19Z","title":"FoundHand: Large-Scale Domain-Specific Learning for Controllable Hand Image Generation","version":2},"reference_index":85,"source":"pdf_text","source_observed_at":"2026-08-11T23:13:39.692895Z"},"links":{"citing_paper":"/paper/2412.02690"},"observation_digest":"sha256:c11a32f4abd161accba6116352d3c8f980a77174a054d14729d9ae99beeeb3a1","observation_id":"cdcf1d90-e449-46e8-a573-9d66ef173bb4","resolution":{"observed_at":"2026-08-11T23:13:40.796604Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2310.15110","last_updated":"2023-10-23T17:18:59Z","snapshot_observed_at":"2026-08-11T13:07:00.745167Z","submitted_at":"2023-10-23T17:18:59Z","title":"Zero123++: a Single Image to Consistent Multi-view Diffusion Base Model","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.15110","snapshot_observed_at":"2026-08-11T23:13:39.697124Z","title":"Zero123++: a single image to consis- tent multi-view diffusion base model","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.02690","last_updated":"2024-12-04T20:51:17Z","snapshot_observed_at":"2026-08-12T15:54:02.294599Z","submitted_at":"2024-12-03T18:58:19Z","title":"FoundHand: Large-Scale Domain-Specific Learning for Controllable Hand Image Generation","version":2},"reference_index":86,"source":"pdf_text","source_observed_at":"2026-08-11T23:13:39.697124Z"},"links":{"cited_paper":"/paper/2310.15110","citing_paper":"/paper/2412.02690"},"observation_digest":"sha256:691b4ced63d1fef7f64ef1452ca4a95b1b7e4edd5ebfd5719f7a8f54f6b3bde2","observation_id":"06ded6e3-1774-4975-bfa1-13ad6388701e","resolution":{"observed_at":"2026-08-11T23:13:39.697124Z","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":"2026-08-11T23:13:40.778200Z","title":"Sangineto, St ´ephane Lathuili `ere, and N","venue":null,"work_id":"c4538bcb-e277-4b1b-b3d3-5f2b2aa17cff","year":2018},"citing_paper":{"arxiv_id":"2412.02690","last_updated":"2024-12-04T20:51:17Z","snapshot_observed_at":"2026-08-12T15:54:02.294599Z","submitted_at":"2024-12-03T18:58:19Z","title":"FoundHand: Large-Scale Domain-Specific Learning for Controllable Hand Image Generation","version":2},"reference_index":87,"source":"pdf_text","source_observed_at":"2026-08-11T23:13:39.701424Z"},"links":{"citing_paper":"/paper/2412.02690"},"observation_digest":"sha256:b5b07b0fb0f68bd681d301174f96881c75349c110da2a6b05bf6015690529cee","observation_id":"7a5ccdfc-02c7-431b-97d6-e0dd911e50ae","resolution":{"observed_at":"2026-08-11T23:13:40.782509Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T23:13:40.763376Z","title":"Constrained 6-dof grasp gener- ation on complex shapes for improved dual-arm manipula- tion, 2024","venue":null,"work_id":"cf86e1be-fe62-40af-8a0e-1f986048f737","year":2024},"citing_paper":{"arxiv_id":"2412.02690","last_updated":"2024-12-04T20:51:17Z","snapshot_observed_at":"2026-08-12T15:54:02.294599Z","submitted_at":"2024-12-03T18:58:19Z","title":"FoundHand: Large-Scale Domain-Specific Learning for Controllable Hand Image Generation","version":2},"reference_index":88,"source":"pdf_text","source_observed_at":"2026-08-11T23:13:39.705457Z"},"links":{"citing_paper":"/paper/2412.02690"},"observation_digest":"sha256:065216c6de8a6174c67b602561bd78dab9598ed8f3b5ef32ed94deff52234dcc","observation_id":"cd641bcc-8a01-46cc-ba19-2798436ec265","resolution":{"observed_at":"2026-08-11T23:13:40.768199Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2404.04643","last_updated":"2024-07-15T15:45:57Z","snapshot_observed_at":"2026-08-13T00:35:55.007476Z","submitted_at":"2024-04-06T14:28:01Z","title":"Constrained 6-DoF Grasp Generation on Complex Shapes for Improved Dual-Arm Manipulation","version":2},"cited_work":{"arxiv_id":"2404.04643","doi":null,"metadata_source":"pith","pith_arxiv_id":"2404.04643","snapshot_observed_at":"2026-08-11T23:13:39.961268Z","title":"Constrained 6-DoF Grasp Generation on Complex Shapes for Improved Dual-Arm Manipulation","venue":"cs.RO","work_id":"75d310dc-855e-4a89-b00a-2b9d0fc944e6","year":2024},"citing_paper":{"arxiv_id":"2412.02690","last_updated":"2024-12-04T20:51:17Z","snapshot_observed_at":"2026-08-12T15:54:02.294599Z","submitted_at":"2024-12-03T18:58:19Z","title":"FoundHand: Large-Scale Domain-Specific Learning for Controllable Hand Image Generation","version":2},"reference_index":89,"source":"pdf_text","source_observed_at":"2026-08-11T23:13:39.709625Z"},"links":{"cited_paper":"/paper/2404.04643","citing_paper":"/paper/2412.02690"},"observation_digest":"sha256:8753bef4aa30a08846689d2edd4a9b9aecdeef25ec1dd287d4f09ec32bc86ce8","observation_id":"ef0cf0ed-8ad8-4f48-82d3-a753e328c1d9","resolution":{"observed_at":"2026-08-11T23:13:39.968213Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T23:13:40.749925Z","title":"The effectiveness of mae pre-pretraining for billion-scale pretraining","venue":null,"work_id":"12f0851b-e3ef-4bc0-bcac-702e8d4200e8","year":null},"citing_paper":{"arxiv_id":"2412.02690","last_updated":"2024-12-04T20:51:17Z","snapshot_observed_at":"2026-08-12T15:54:02.294599Z","submitted_at":"2024-12-03T18:58:19Z","title":"FoundHand: Large-Scale Domain-Specific Learning for Controllable Hand Image Generation","version":2},"reference_index":90,"source":"pdf_text","source_observed_at":"2026-08-11T23:13:39.714134Z"},"links":{"citing_paper":"/paper/2412.02690"},"observation_digest":"sha256:9f8d97d2c96b8db6a0e91ff2542939bce00e677c728d1d5c084962a0c7c81ae5","observation_id":"1f8d472b-fe95-4139-b07b-24fb2a547cae","resolution":{"observed_at":"2026-08-11T23:13:40.754359Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T23:13:40.736744Z","title":"Weiss, Niru Mah- eswaranathan, and Surya Ganguli","venue":null,"work_id":"82bf0273-23d0-4879-9e6d-d9b2a664a536","year":2015},"citing_paper":{"arxiv_id":"2412.02690","last_updated":"2024-12-04T20:51:17Z","snapshot_observed_at":"2026-08-12T15:54:02.294599Z","submitted_at":"2024-12-03T18:58:19Z","title":"FoundHand: Large-Scale Domain-Specific Learning for Controllable Hand Image Generation","version":2},"reference_index":91,"source":"pdf_text","source_observed_at":"2026-08-11T23:13:39.718355Z"},"links":{"citing_paper":"/paper/2412.02690"},"observation_digest":"sha256:fe2bfd7c3962b4ae66dced7beae8370348386ae18f6fca18d59986b68dc7d805","observation_id":"5bf720e2-4b64-4c18-9fa2-5b249691785f","resolution":{"observed_at":"2026-08-11T23:13:40.741091Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2010.02502","last_updated":"2022-10-05T20:19:21Z","snapshot_observed_at":"2026-08-11T15:38:14.931716Z","submitted_at":"2020-10-06T06:15:51Z","title":"Denoising Diffusion Implicit Models","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2010.02502","snapshot_observed_at":"2026-08-11T23:13:39.722806Z","title":"Denoising diffusion implicit models","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2412.02690","last_updated":"2024-12-04T20:51:17Z","snapshot_observed_at":"2026-08-12T15:54:02.294599Z","submitted_at":"2024-12-03T18:58:19Z","title":"FoundHand: Large-Scale Domain-Specific Learning for Controllable Hand Image Generation","version":2},"reference_index":92,"source":"pdf_text","source_observed_at":"2026-08-11T23:13:39.722806Z"},"links":{"cited_paper":"/paper/2010.02502","citing_paper":"/paper/2412.02690"},"observation_digest":"sha256:de96d0e9b794e60001cda99f5c20d2932e3ef07c9598381e592f6958179d1011","observation_id":"bbe140d3-1f68-4002-8039-1376552457ed","resolution":{"observed_at":"2026-08-11T23:13:39.722806Z","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":"2026-08-11T23:13:40.722010Z","title":"Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole","venue":null,"work_id":"a64ce675-fbe8-44dd-ba88-6b85bf6cfdd9","year":2021},"citing_paper":{"arxiv_id":"2412.02690","last_updated":"2024-12-04T20:51:17Z","snapshot_observed_at":"2026-08-12T15:54:02.294599Z","submitted_at":"2024-12-03T18:58:19Z","title":"FoundHand: Large-Scale Domain-Specific Learning for Controllable Hand Image Generation","version":2},"reference_index":93,"source":"pdf_text","source_observed_at":"2026-08-11T23:13:39.727673Z"},"links":{"citing_paper":"/paper/2412.02690"},"observation_digest":"sha256:2cb31417a6ca608983cbd0fc0dd74e2403e6d8e46127f40a070cd24257004913","observation_id":"60c43dfb-f75f-4553-acaa-c5b40b48d258","resolution":{"observed_at":"2026-08-11T23:13:40.726698Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T23:13:40.706395Z","title":"Controlling the world by sleight of hand, 2024","venue":null,"work_id":"881cf192-4424-4cff-98ce-3f1b41c872f6","year":2024},"citing_paper":{"arxiv_id":"2412.02690","last_updated":"2024-12-04T20:51:17Z","snapshot_observed_at":"2026-08-12T15:54:02.294599Z","submitted_at":"2024-12-03T18:58:19Z","title":"FoundHand: Large-Scale Domain-Specific Learning for Controllable Hand Image Generation","version":2},"reference_index":94,"source":"pdf_text","source_observed_at":"2026-08-11T23:13:39.732086Z"},"links":{"citing_paper":"/paper/2412.02690"},"observation_digest":"sha256:60bb3fa9226b4a10d8be3fc66dd8bff22b3b7749c43cb31deafa5ba9e899c7d2","observation_id":"bb3dcc26-390a-4e6b-8627-c17dc214570a","resolution":{"observed_at":"2026-08-11T23:13:40.711500Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T23:13:40.691353Z","title":"Dimensionx: Create any 3d and 4d scenes from a single image with con- trollable video diffusion, 2024","venue":null,"work_id":"b68dbe9d-d05e-4b5c-8707-351615b66712","year":2024},"citing_paper":{"arxiv_id":"2412.02690","last_updated":"2024-12-04T20:51:17Z","snapshot_observed_at":"2026-08-12T15:54:02.294599Z","submitted_at":"2024-12-03T18:58:19Z","title":"FoundHand: Large-Scale Domain-Specific Learning for Controllable Hand Image Generation","version":2},"reference_index":95,"source":"pdf_text","source_observed_at":"2026-08-11T23:13:39.736468Z"},"links":{"citing_paper":"/paper/2412.02690"},"observation_digest":"sha256:26b675204ce8df08dcb402584fa18f0c080e85406e08050a08d871a02bead4ee","observation_id":"d86dc201-adfb-4c8b-898c-63ae9213b638","resolution":{"observed_at":"2026-08-11T23:13:40.696219Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T23:13:40.676511Z","title":"Anycontrol: Create your artwork with versatile control on text-to-image generation, 2024","venue":null,"work_id":"0e08ce4a-e637-4f8e-b51f-e87b540aada9","year":2024},"citing_paper":{"arxiv_id":"2412.02690","last_updated":"2024-12-04T20:51:17Z","snapshot_observed_at":"2026-08-12T15:54:02.294599Z","submitted_at":"2024-12-03T18:58:19Z","title":"FoundHand: Large-Scale Domain-Specific Learning for Controllable Hand Image Generation","version":2},"reference_index":96,"source":"pdf_text","source_observed_at":"2026-08-11T23:13:39.741198Z"},"links":{"citing_paper":"/paper/2412.02690"},"observation_digest":"sha256:8f543ce4b394315605e0850e1ed489a6bfbc5b0db82ce7783f0a39fb736b7e6e","observation_id":"9c679292-28ef-4c34-929a-060d8febe1cb","resolution":{"observed_at":"2026-08-11T23:13:40.681304Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T23:13:40.661068Z","title":"Controllable 3d generative adversarial face model via disentangling shape and appearance","venue":null,"work_id":"88b3937a-452f-47d6-86e5-1d3e6eff95ae","year":2023},"citing_paper":{"arxiv_id":"2412.02690","last_updated":"2024-12-04T20:51:17Z","snapshot_observed_at":"2026-08-12T15:54:02.294599Z","submitted_at":"2024-12-03T18:58:19Z","title":"FoundHand: Large-Scale Domain-Specific Learning for Controllable Hand Image Generation","version":2},"reference_index":97,"source":"pdf_text","source_observed_at":"2026-08-11T23:13:39.745579Z"},"links":{"citing_paper":"/paper/2412.02690"},"observation_digest":"sha256:b862297de5340a24feacb9490a3c37efc881f144be4bc0c2f8990045ef12fffe","observation_id":"744b8409-1f51-4fdc-8f09-1cfdf9f31138","resolution":{"observed_at":"2026-08-11T23:13:40.665980Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T23:13:40.645168Z","title":"Total generate: Cycle in cy- cle generative adversarial networks for generating human faces, hands, bodies, and natural scenes","venue":null,"work_id":"f87f5ba3-bd10-44ab-a68b-a48a84609b0d","year":2022},"citing_paper":{"arxiv_id":"2412.02690","last_updated":"2024-12-04T20:51:17Z","snapshot_observed_at":"2026-08-12T15:54:02.294599Z","submitted_at":"2024-12-03T18:58:19Z","title":"FoundHand: Large-Scale Domain-Specific Learning for Controllable Hand Image Generation","version":2},"reference_index":98,"source":"pdf_text","source_observed_at":"2026-08-11T23:13:39.750062Z"},"links":{"citing_paper":"/paper/2412.02690"},"observation_digest":"sha256:3e217b81600d949f276088ac683011bb8e4009ab92dcdbbae5562ca4650395fe","observation_id":"5bd4bc85-fab6-4292-b334-02727af3e012","resolution":{"observed_at":"2026-08-11T23:13:40.650432Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T23:13:40.629577Z","title":null,"venue":null,"work_id":"4cd1a59e-08d9-4bd2-8dcf-ebbd393ce35d","year":2018},"citing_paper":{"arxiv_id":"2412.02690","last_updated":"2024-12-04T20:51:17Z","snapshot_observed_at":"2026-08-12T15:54:02.294599Z","submitted_at":"2024-12-03T18:58:19Z","title":"FoundHand: Large-Scale Domain-Specific Learning for Controllable Hand Image Generation","version":2},"reference_index":99,"source":"pdf_text","source_observed_at":"2026-08-11T23:13:39.754286Z"},"links":{"citing_paper":"/paper/2412.02690"},"observation_digest":"sha256:1fe28e400895209e04b7b2fdf6aefad3a6a63d1deee960f12a765cab849b30dd","observation_id":"d5cac24e-ecb4-4c56-9415-5e4cee056c54","resolution":{"observed_at":"2026-08-11T23:13:40.634330Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T23:13:40.614781Z","title":"Real-time continuous pose recovery of human hands using convolutional networks","venue":null,"work_id":"eac64e65-e748-463d-b009-8ff0cebaed63","year":2014},"citing_paper":{"arxiv_id":"2412.02690","last_updated":"2024-12-04T20:51:17Z","snapshot_observed_at":"2026-08-12T15:54:02.294599Z","submitted_at":"2024-12-03T18:58:19Z","title":"FoundHand: Large-Scale Domain-Specific Learning for Controllable Hand Image Generation","version":2},"reference_index":100,"source":"pdf_text","source_observed_at":"2026-08-11T23:13:39.759082Z"},"links":{"citing_paper":"/paper/2412.02690"},"observation_digest":"sha256:4003681f0bb8cd0f41597ba353c5193942cabc0d6c7a80ea179d10640e3aeb7b","observation_id":"6b8eeb90-7c0f-4564-ba33-8a6a8d37aa2f","resolution":{"observed_at":"2026-08-11T23:13:40.619441Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T23:13:40.599730Z","title":"Realishuman: A two- stage approach for refining malformed human parts in gen- erated images, 2024","venue":null,"work_id":"0d9ccd43-eb84-4850-8f93-a089d7e4e5d4","year":2024},"citing_paper":{"arxiv_id":"2412.02690","last_updated":"2024-12-04T20:51:17Z","snapshot_observed_at":"2026-08-12T15:54:02.294599Z","submitted_at":"2024-12-03T18:58:19Z","title":"FoundHand: Large-Scale Domain-Specific Learning for Controllable Hand Image Generation","version":2},"reference_index":101,"source":"pdf_text","source_observed_at":"2026-08-11T23:13:39.763533Z"},"links":{"citing_paper":"/paper/2412.02690"},"observation_digest":"sha256:d988fda64d21ca48bbf6b9b3f8254ce8e8363958dd8fa889213be9dfe5984480","observation_id":"c8fa6d32-cfe4-4702-9ea3-8d5862db6946","resolution":{"observed_at":"2026-08-11T23:13:40.604560Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T23:13:40.585746Z","title":"Hall, and Shimin Hu","venue":null,"work_id":"0c8b9523-4772-44f9-9c7a-dff1d524e230","year":2019},"citing_paper":{"arxiv_id":"2412.02690","last_updated":"2024-12-04T20:51:17Z","snapshot_observed_at":"2026-08-12T15:54:02.294599Z","submitted_at":"2024-12-03T18:58:19Z","title":"FoundHand: Large-Scale Domain-Specific Learning for Controllable Hand Image Generation","version":2},"reference_index":102,"source":"pdf_text","source_observed_at":"2026-08-11T23:13:39.767834Z"},"links":{"citing_paper":"/paper/2412.02690"},"observation_digest":"sha256:9cc97175ee9f6ecdd2650124cd6e42d8fb8a5e976a8c09332dfc6fd01009ebd8","observation_id":"bc7bfdbd-8f15-446c-8af6-db621650c9be","resolution":{"observed_at":"2026-08-11T23:13:40.589826Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2412.02690","last_updated":"2024-12-04T20:51:17Z","latest_version":2,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-12T15:54:02.294599Z","submitted_at":"2024-12-03T18:58:19Z","title":"FoundHand: Large-Scale Domain-Specific Learning for Controllable Hand Image Generation"},"reference_resolution":{"displayed":100,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":57,"verified_exact":7,"verified_fuzzy":36},"total_outbound_references":113},"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":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"thesis":"As of 13 August 2026, this Paper Citation Record lists 100 of 113 outbound references and 1 inbound Pith citation observation for arXiv:2412.02690."}