{"as_of":"2026-08-12T15:43:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:3ab312ec086096370d2fb20d3556e6e46307302f9c9928138bcaf51db7a2f72f","coverage":[{"denominator":124,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":100,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-11T15:18:18.908204Z","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":"2026-08-12T06:34:41.77262+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":"2026-08-06T21:59:10.706905Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"pith","source_observed_at":"2026-08-06T21:59:11.785013Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2412.11170","last_updated":"2025-07-27T09:18:49Z","snapshot_observed_at":"2026-08-11T22:10:22.581543Z","submitted_at":"2024-12-15T12:41:44Z","title":"Benchmarking and Learning Multi-Dimensional Quality Evaluator for Text-to-3D Generation","version":2},"cited_work":{"arxiv_id":"2412.11170","doi":null,"metadata_source":"pith","pith_arxiv_id":"2412.11170","snapshot_observed_at":"2026-08-06T21:59:11.785013Z","title":"Benchmarking and Learning Multi-Dimensional Quality Evaluator for Text-to-3D Generation","venue":"cs.CV","work_id":"00b813f7-f650-43f5-9598-4037c7899b49","year":2024},"citing_paper":{"arxiv_id":"2506.22902","last_updated":"2025-06-28T14:34:24Z","snapshot_observed_at":"2026-08-09T12:02:56.766009Z","submitted_at":"2025-06-28T14:34:24Z","title":"Point Cloud Compression and Objective Quality Assessment: A Survey","version":1},"reference_index":216,"source":"pdf_text","source_observed_at":"2026-08-06T21:59:10.706905Z"},"links":{"cited_paper":"/paper/2412.11170","citing_paper":"/paper/2506.22902"},"observation_digest":"sha256:c2077fb57dd0d335f41f7925cf3254e9dd80144fb6220ff992bc4b0e9cfe0bb9","observation_id":"f4e9473b-a0eb-45e8-8f0a-4b424f34b3c3","resolution":{"observed_at":"2026-08-06T21:59:11.889910Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2412.11170/citation-record","integrity":"/paper/2412.11170/integrity","json":"/paper/2412.11170/citation-record.json","paper":"/paper/2412.11170"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T15:18:18.414550Z","title":"Lara: Efficient large-baseline radiance fields","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.11170","last_updated":"2025-07-27T09:18:49Z","snapshot_observed_at":"2026-08-11T22:10:22.581543Z","submitted_at":"2024-12-15T12:41:44Z","title":"Benchmarking and Learning Multi-Dimensional Quality Evaluator for Text-to-3D Generation","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-11T15:18:18.414550Z"},"links":{"citing_paper":"/paper/2412.11170"},"observation_digest":"sha256:4b583be8280b60ca932b3205a0e5961badb6bd7475e374b7f3a41f642033d900","observation_id":"74583afd-a2aa-4a65-922f-5b7b57115085","resolution":{"observed_at":"2026-08-11T15:18:18.414550Z","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":"2026-08-11T15:18:18.420420Z","title":"Sculpt3d: Multi-view consistent text-to-3d generation with sparse 3d prior","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.11170","last_updated":"2025-07-27T09:18:49Z","snapshot_observed_at":"2026-08-11T22:10:22.581543Z","submitted_at":"2024-12-15T12:41:44Z","title":"Benchmarking and Learning Multi-Dimensional Quality Evaluator for Text-to-3D Generation","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-11T15:18:18.420420Z"},"links":{"citing_paper":"/paper/2412.11170"},"observation_digest":"sha256:513f6f289ab80b628560830bdf0fa81ee42ce083331cba782c00d093dbd621ba","observation_id":"3ff2e8c3-a14b-4309-811f-e434d29da60a","resolution":{"observed_at":"2026-08-11T15:18:18.420420Z","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":"2026-08-11T15:18:18.425854Z","title":"Vp3d: Unleashing 2d visual prompt for text-to-3d gen- eration","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.11170","last_updated":"2025-07-27T09:18:49Z","snapshot_observed_at":"2026-08-11T22:10:22.581543Z","submitted_at":"2024-12-15T12:41:44Z","title":"Benchmarking and Learning Multi-Dimensional Quality Evaluator for Text-to-3D Generation","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-11T15:18:18.425854Z"},"links":{"citing_paper":"/paper/2412.11170"},"observation_digest":"sha256:64911a43bc6cbec350d8414ec2258ded9a453d0411c20d041c6b85788856e1cb","observation_id":"189852f4-36e2-4bb5-8aa5-6a53ff75312a","resolution":{"observed_at":"2026-08-11T15:18:18.425854Z","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":"2026-08-11T15:18:18.430996Z","title":"Objaverse-xl: A universe of 10m+ 3d objects","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.11170","last_updated":"2025-07-27T09:18:49Z","snapshot_observed_at":"2026-08-11T22:10:22.581543Z","submitted_at":"2024-12-15T12:41:44Z","title":"Benchmarking and Learning Multi-Dimensional Quality Evaluator for Text-to-3D Generation","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-11T15:18:18.430996Z"},"links":{"citing_paper":"/paper/2412.11170"},"observation_digest":"sha256:cf6383b479d9f0fd5c5415be39a53c9e4be1846c3222d743ec8af25137055789","observation_id":"e5d46f23-02d7-4bd9-8c4d-4cd0eed27509","resolution":{"observed_at":"2026-08-11T15:18:18.430996Z","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":"2026-08-11T15:18:18.435968Z","title":"Objaverse: A universe of annotated 3d objects","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.11170","last_updated":"2025-07-27T09:18:49Z","snapshot_observed_at":"2026-08-11T22:10:22.581543Z","submitted_at":"2024-12-15T12:41:44Z","title":"Benchmarking and Learning Multi-Dimensional Quality Evaluator for Text-to-3D Generation","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-11T15:18:18.435968Z"},"links":{"citing_paper":"/paper/2412.11170"},"observation_digest":"sha256:f8af2350de6b53f53107c9ee3f31d978519e82f3aa6e13db3e872797d683a09f","observation_id":"5236ed51-1005-4ff4-a9d4-e0a0a02df5ad","resolution":{"observed_at":"2026-08-11T15:18:18.435968Z","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":"2026-08-11T15:18:18.440844Z","title":"Imagenet: A large-scale hierarchical image database","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2412.11170","last_updated":"2025-07-27T09:18:49Z","snapshot_observed_at":"2026-08-11T22:10:22.581543Z","submitted_at":"2024-12-15T12:41:44Z","title":"Benchmarking and Learning Multi-Dimensional Quality Evaluator for Text-to-3D Generation","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-11T15:18:18.440844Z"},"links":{"citing_paper":"/paper/2412.11170"},"observation_digest":"sha256:f1b561007565b72a5a882a032a45b63c93625e7185140bd0e30af95ae4cfcf22","observation_id":"29977eb5-0847-4df4-88a1-efe8f22b0606","resolution":{"observed_at":"2026-08-11T15:18:18.440844Z","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":"2026-08-11T15:18:18.445788Z","title":"Interactive3d: Create what you want by interactive 3d generation","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.11170","last_updated":"2025-07-27T09:18:49Z","snapshot_observed_at":"2026-08-11T22:10:22.581543Z","submitted_at":"2024-12-15T12:41:44Z","title":"Benchmarking and Learning Multi-Dimensional Quality Evaluator for Text-to-3D Generation","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-11T15:18:18.445788Z"},"links":{"citing_paper":"/paper/2412.11170"},"observation_digest":"sha256:361e241c86d1367d14839db93a07a7d2f1288d59b50cc907f2719c6e4e1bf9f7","observation_id":"deb3a445-4ace-4031-82f8-790e9eba997b","resolution":{"observed_at":"2026-08-11T15:18:18.445788Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2010.11929","last_updated":"2021-06-03T13:08:56Z","snapshot_observed_at":"2026-08-10T01:12:16.468283Z","submitted_at":"2020-10-22T17:55:59Z","title":"An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2010.11929","snapshot_observed_at":"2026-08-11T15:18:18.450699Z","title":"An image is worth 16x16 words: Transformers for image recognition at scale","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2412.11170","last_updated":"2025-07-27T09:18:49Z","snapshot_observed_at":"2026-08-11T22:10:22.581543Z","submitted_at":"2024-12-15T12:41:44Z","title":"Benchmarking and Learning Multi-Dimensional Quality Evaluator for Text-to-3D Generation","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-11T15:18:18.450699Z"},"links":{"cited_paper":"/paper/2010.11929","citing_paper":"/paper/2412.11170"},"observation_digest":"sha256:2519e440b61de46531da5be6494f14dc046918bd5fd230acaf710641810a2234","observation_id":"16834a20-fd78-47c7-9359-34e6a35557f4","resolution":{"observed_at":"2026-08-11T15:18:18.450699Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2008.02312","last_updated":"2020-08-19T06:04:28Z","snapshot_observed_at":"2026-08-10T01:49:21.149915Z","submitted_at":"2020-08-05T18:42:33Z","title":"Axiom-based Grad-CAM: Towards Accurate Visualization and Explanation of CNNs","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2008.02312","snapshot_observed_at":"2026-08-11T15:18:18.456266Z","title":"Axiom-based grad-cam: To- wards accurate visualization and explanation of cnns","venue":null,"work_id":null,"year":2008},"citing_paper":{"arxiv_id":"2412.11170","last_updated":"2025-07-27T09:18:49Z","snapshot_observed_at":"2026-08-11T22:10:22.581543Z","submitted_at":"2024-12-15T12:41:44Z","title":"Benchmarking and Learning Multi-Dimensional Quality Evaluator for Text-to-3D Generation","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-11T15:18:18.456266Z"},"links":{"cited_paper":"/paper/2008.02312","citing_paper":"/paper/2412.11170"},"observation_digest":"sha256:e2056481d01f5ed5a8075547d71ce890803596296ed64dc59f30c4e7a93c336b","observation_id":"1f27aa50-f5f1-4bd6-b089-2bacd59af431","resolution":{"observed_at":"2026-08-11T15:18:18.456266Z","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":"2026-08-11T15:18:18.461368Z","title":"Shapecrafter: A recursive text-conditioned 3d shape generation model","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.11170","last_updated":"2025-07-27T09:18:49Z","snapshot_observed_at":"2026-08-11T22:10:22.581543Z","submitted_at":"2024-12-15T12:41:44Z","title":"Benchmarking and Learning Multi-Dimensional Quality Evaluator for Text-to-3D Generation","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-11T15:18:18.461368Z"},"links":{"citing_paper":"/paper/2412.11170"},"observation_digest":"sha256:79610ed4c388222d54b8e038a260d2794e7f44b9b9f3a4a8ba606478586fe790","observation_id":"b1d35daf-d430-497d-a20e-7ef58b9a3ed7","resolution":{"observed_at":"2026-08-11T15:18:18.461368Z","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":"2026-08-11T15:18:18.466252Z","title":"Get3d: A generative model of high quality 3d tex- tured shapes learned from images","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.11170","last_updated":"2025-07-27T09:18:49Z","snapshot_observed_at":"2026-08-11T22:10:22.581543Z","submitted_at":"2024-12-15T12:41:44Z","title":"Benchmarking and Learning Multi-Dimensional Quality Evaluator for Text-to-3D Generation","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-11T15:18:18.466252Z"},"links":{"citing_paper":"/paper/2412.11170"},"observation_digest":"sha256:e4a2e1fed1de6e3db0572403e49d63217a377e74a81eb67dd637cb0f53b484c7","observation_id":"79472e1e-55bc-4279-8caf-14737ee2461a","resolution":{"observed_at":"2026-08-11T15:18:18.466252Z","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":"2026-08-11T15:18:18.470797Z","title":"Hypernet- works","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.11170","last_updated":"2025-07-27T09:18:49Z","snapshot_observed_at":"2026-08-11T22:10:22.581543Z","submitted_at":"2024-12-15T12:41:44Z","title":"Benchmarking and Learning Multi-Dimensional Quality Evaluator for Text-to-3D Generation","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-11T15:18:18.470797Z"},"links":{"citing_paper":"/paper/2412.11170"},"observation_digest":"sha256:c854e29e009b452b3ed5312a17c2a4990fbde7564975ee1394f3d5ecd2eaaca3","observation_id":"25808075-45d9-46ba-b2d4-3170006da884","resolution":{"observed_at":"2026-08-11T15:18:18.470797Z","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":"2026-08-11T15:18:18.476081Z","title":"Deep residual learning for image recognition","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2412.11170","last_updated":"2025-07-27T09:18:49Z","snapshot_observed_at":"2026-08-11T22:10:22.581543Z","submitted_at":"2024-12-15T12:41:44Z","title":"Benchmarking and Learning Multi-Dimensional Quality Evaluator for Text-to-3D Generation","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-11T15:18:18.476081Z"},"links":{"citing_paper":"/paper/2412.11170"},"observation_digest":"sha256:94339a03c5fe1b190f863e03c00cfab97d10661cf14f6109c5b83a69e90e46af","observation_id":"09fc211a-bfb8-438d-b186-d5d37dc165ca","resolution":{"observed_at":"2026-08-11T15:18:18.476081Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.02977","last_updated":"2024-04-17T09:09:17Z","snapshot_observed_at":"2026-07-06T16:27:47.445841Z","submitted_at":"2023-10-04T17:12:18Z","title":"T$^3$Bench: Benchmarking Current Progress in Text-to-3D Generation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.02977","snapshot_observed_at":"2026-08-11T15:18:18.480830Z","title":"T3bench: Benchmarking current progress in text-to-3d gen- eration","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.11170","last_updated":"2025-07-27T09:18:49Z","snapshot_observed_at":"2026-08-11T22:10:22.581543Z","submitted_at":"2024-12-15T12:41:44Z","title":"Benchmarking and Learning Multi-Dimensional Quality Evaluator for Text-to-3D Generation","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-11T15:18:18.480830Z"},"links":{"cited_paper":"/paper/2310.02977","citing_paper":"/paper/2412.11170"},"observation_digest":"sha256:4752ec186018ce4601d634d4fbd300f7eba19b11acdf76169bb96f00b9d72c17","observation_id":"8ec5e8a9-dc53-4a9f-99db-a89d71441512","resolution":{"observed_at":"2026-08-11T15:18:18.480830Z","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":"2026-08-11T15:18:18.486045Z","title":"Mmpi: a flexible radiance field representation by multiple multi-plane images blend- ing","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.11170","last_updated":"2025-07-27T09:18:49Z","snapshot_observed_at":"2026-08-11T22:10:22.581543Z","submitted_at":"2024-12-15T12:41:44Z","title":"Benchmarking and Learning Multi-Dimensional Quality Evaluator for Text-to-3D Generation","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-11T15:18:18.486045Z"},"links":{"citing_paper":"/paper/2412.11170"},"observation_digest":"sha256:5b6db05154e6bb17e2635dbe8da30d84533da4a6542639f63448cb670537ca9c","observation_id":"2cc2f9e6-9375-4f6b-9f88-2fa7e45e3928","resolution":{"observed_at":"2026-08-11T15:18:18.486045Z","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":"2026-08-11T15:18:18.490721Z","title":"CLIPScore: A reference-free evaluation metric for image captioning","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.11170","last_updated":"2025-07-27T09:18:49Z","snapshot_observed_at":"2026-08-11T22:10:22.581543Z","submitted_at":"2024-12-15T12:41:44Z","title":"Benchmarking and Learning Multi-Dimensional Quality Evaluator for Text-to-3D Generation","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-11T15:18:18.490721Z"},"links":{"citing_paper":"/paper/2412.11170"},"observation_digest":"sha256:c580cf817614a883246ea77b2fef4bd563108b12ad0e3fbd6b3b170c834d4453","observation_id":"822fee58-db14-42ff-92da-2f572165e5bd","resolution":{"observed_at":"2026-08-11T15:18:18.490721Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.02234","last_updated":"2024-05-07T03:25:50Z","snapshot_observed_at":"2026-07-06T17:39:20.748463Z","submitted_at":"2024-03-04T17:26:28Z","title":"3DTopia: Large Text-to-3D Generation Model with Hybrid Diffusion Priors","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.02234","snapshot_observed_at":"2026-08-11T15:18:18.495855Z","title":"3dtopia: Large text-to-3d generation model with hybrid diffusion priors","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.11170","last_updated":"2025-07-27T09:18:49Z","snapshot_observed_at":"2026-08-11T22:10:22.581543Z","submitted_at":"2024-12-15T12:41:44Z","title":"Benchmarking and Learning Multi-Dimensional Quality Evaluator for Text-to-3D Generation","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-11T15:18:18.495855Z"},"links":{"cited_paper":"/paper/2403.02234","citing_paper":"/paper/2412.11170"},"observation_digest":"sha256:9cbf16bcde1e7a1fb28397cbdc53ca8130af72f2e42329cacc993621811646ad","observation_id":"76d0f5f0-63b4-4c8f-ba7b-688a682a7e42","resolution":{"observed_at":"2026-08-11T15:18:18.495855Z","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":"2026-08-11T15:18:18.501211Z","title":"Debi- asing scores and prompts of 2d diffusion for view-consistent text-to-3d generation","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.11170","last_updated":"2025-07-27T09:18:49Z","snapshot_observed_at":"2026-08-11T22:10:22.581543Z","submitted_at":"2024-12-15T12:41:44Z","title":"Benchmarking and Learning Multi-Dimensional Quality Evaluator for Text-to-3D Generation","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-11T15:18:18.501211Z"},"links":{"citing_paper":"/paper/2412.11170"},"observation_digest":"sha256:7609b9f0ddd16f0d79f4628d54eadde4b8ee0c573cd571c0a14c3b1c31a9198a","observation_id":"fc9d140f-5fcc-450b-a236-c949094cd7c0","resolution":{"observed_at":"2026-08-11T15:18:18.501211Z","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":"2026-08-11T15:18:18.506093Z","title":"Lrm: Large reconstruction model for single image to 3d","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.11170","last_updated":"2025-07-27T09:18:49Z","snapshot_observed_at":"2026-08-11T22:10:22.581543Z","submitted_at":"2024-12-15T12:41:44Z","title":"Benchmarking and Learning Multi-Dimensional Quality Evaluator for Text-to-3D Generation","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-11T15:18:18.506093Z"},"links":{"citing_paper":"/paper/2412.11170"},"observation_digest":"sha256:f2e9ba3b64761e19d3157de5fd1241248bf8ec7e898e89ac8f06b880efac4bf5","observation_id":"10c5e501-03eb-46dd-8650-ee5454aa0308","resolution":{"observed_at":"2026-08-11T15:18:18.506093Z","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":"2026-08-11T15:18:18.511073Z","title":"Towards trans- parent deep image aesthetics assessment with tag-based con- tent descriptors","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.11170","last_updated":"2025-07-27T09:18:49Z","snapshot_observed_at":"2026-08-11T22:10:22.581543Z","submitted_at":"2024-12-15T12:41:44Z","title":"Benchmarking and Learning Multi-Dimensional Quality Evaluator for Text-to-3D Generation","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-11T15:18:18.511073Z"},"links":{"citing_paper":"/paper/2412.11170"},"observation_digest":"sha256:93ae30d6c94fdf477e457226669221a009a2041b5c48d7979785d3da61044c5d","observation_id":"ce1c8734-4bbb-47aa-9875-f4e7d679d63d","resolution":{"observed_at":"2026-08-11T15:18:18.511073Z","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":"2026-08-11T15:18:18.517042Z","title":"Nerf-texture: Texture synthesis with neural radi- ance fields","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.11170","last_updated":"2025-07-27T09:18:49Z","snapshot_observed_at":"2026-08-11T22:10:22.581543Z","submitted_at":"2024-12-15T12:41:44Z","title":"Benchmarking and Learning Multi-Dimensional Quality Evaluator for Text-to-3D Generation","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-11T15:18:18.517042Z"},"links":{"citing_paper":"/paper/2412.11170"},"observation_digest":"sha256:3b74b069bd83f270e7a29428bb168b7a15229edb1ffd95566366fb221f20553c","observation_id":"d8be5318-5ef2-43ea-ab7d-381e9866207d","resolution":{"observed_at":"2026-08-11T15:18:18.517042Z","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":"2026-08-11T15:18:18.521919Z","title":"Make-a-shape: a ten-million-scale 3D shape model","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.11170","last_updated":"2025-07-27T09:18:49Z","snapshot_observed_at":"2026-08-11T22:10:22.581543Z","submitted_at":"2024-12-15T12:41:44Z","title":"Benchmarking and Learning Multi-Dimensional Quality Evaluator for Text-to-3D Generation","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-11T15:18:18.521919Z"},"links":{"citing_paper":"/paper/2412.11170"},"observation_digest":"sha256:563604a83940bb280e6e411c13463713d321f4f4f94512f35b3a614fd4fbe192","observation_id":"113eb70d-6b90-46d2-a468-f20e88e8e3a6","resolution":{"observed_at":"2026-08-11T15:18:18.521919Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.08488","last_updated":"2024-06-12T17:59:52Z","snapshot_observed_at":"2026-08-09T08:19:55.542843Z","submitted_at":"2024-06-12T17:59:52Z","title":"ICE-G: Image Conditional Editing of 3D Gaussian Splats","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.08488","snapshot_observed_at":"2026-08-11T15:18:18.526696Z","title":"Ice-g: Image conditional editing of 3d gaussian splats","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.11170","last_updated":"2025-07-27T09:18:49Z","snapshot_observed_at":"2026-08-11T22:10:22.581543Z","submitted_at":"2024-12-15T12:41:44Z","title":"Benchmarking and Learning Multi-Dimensional Quality Evaluator for Text-to-3D Generation","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-11T15:18:18.526696Z"},"links":{"cited_paper":"/paper/2406.08488","citing_paper":"/paper/2412.11170"},"observation_digest":"sha256:b820b01dede19ca2dc6c452f222e4127cde093017299ec04d04b938670ea05a0","observation_id":"b1a330cb-46e9-442d-bb5d-effad528ea5a","resolution":{"observed_at":"2026-08-11T15:18:18.526696Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2305.02463","last_updated":"2023-05-03T23:59:13Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-05-03T23:59:13Z","title":"Shap-E: Generating Conditional 3D Implicit Functions","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.02463","snapshot_observed_at":"2026-08-11T15:18:18.531785Z","title":"Shap-e: Generat- ing conditional 3d implicit functions","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.11170","last_updated":"2025-07-27T09:18:49Z","snapshot_observed_at":"2026-08-11T22:10:22.581543Z","submitted_at":"2024-12-15T12:41:44Z","title":"Benchmarking and Learning Multi-Dimensional Quality Evaluator for Text-to-3D Generation","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-11T15:18:18.531785Z"},"links":{"cited_paper":"/paper/2305.02463","citing_paper":"/paper/2412.11170"},"observation_digest":"sha256:d8ce49b6c18748f907fe925a86bee9bb649a16aed22fbf33cf5a2a2fb92afa78","observation_id":"6d72c20e-ff94-408a-85d8-cbcde6b23d2e","resolution":{"observed_at":"2026-08-11T15:18:18.531785Z","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":"2026-08-11T15:18:18.537907Z","title":"3d gaussian splatting for real-time radiance field rendering","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.11170","last_updated":"2025-07-27T09:18:49Z","snapshot_observed_at":"2026-08-11T22:10:22.581543Z","submitted_at":"2024-12-15T12:41:44Z","title":"Benchmarking and Learning Multi-Dimensional Quality Evaluator for Text-to-3D Generation","version":2},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-11T15:18:18.537907Z"},"links":{"citing_paper":"/paper/2412.11170"},"observation_digest":"sha256:1b92c4884e624162c010ef047aaae9c005dbe059caee1bef2fdc2498022083e9","observation_id":"4fa014b6-3c8a-4c9e-8ad1-6be9473438e0","resolution":{"observed_at":"2026-08-11T15:18:18.537907Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1412.6980","last_updated":"2017-01-30T01:27:54Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2014-12-22T13:54:29Z","title":"Adam: A Method for Stochastic Optimization","version":9},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1412.6980","snapshot_observed_at":"2026-08-11T15:18:18.542479Z","title":"Adam: A method for stochastic optimization","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.11170","last_updated":"2025-07-27T09:18:49Z","snapshot_observed_at":"2026-08-11T22:10:22.581543Z","submitted_at":"2024-12-15T12:41:44Z","title":"Benchmarking and Learning Multi-Dimensional Quality Evaluator for Text-to-3D Generation","version":2},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-11T15:18:18.542479Z"},"links":{"cited_paper":"/paper/1412.6980","citing_paper":"/paper/2412.11170"},"observation_digest":"sha256:4a0a3650921899b75fbfe889be25b3f549827b0427425328ff25f290f09935a3","observation_id":"9453ec20-bb40-48f8-a4eb-bd1950ca714b","resolution":{"observed_at":"2026-08-11T15:18:18.542479Z","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":"2026-08-11T15:18:18.547868Z","title":"Evaluating and improving composi- tional text-to-visual generation","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.11170","last_updated":"2025-07-27T09:18:49Z","snapshot_observed_at":"2026-08-11T22:10:22.581543Z","submitted_at":"2024-12-15T12:41:44Z","title":"Benchmarking and Learning Multi-Dimensional Quality Evaluator for Text-to-3D Generation","version":2},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-11T15:18:18.547868Z"},"links":{"citing_paper":"/paper/2412.11170"},"observation_digest":"sha256:72395e35caf24b68504fad2efc89529ebe1325cd7675d94e9c69cf30b04ed490","observation_id":"1dc509a4-9bfb-4492-977c-821a49437b21","resolution":{"observed_at":"2026-08-11T15:18:18.547868Z","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":"2026-08-11T15:18:18.552594Z","title":"Aigiqa-20k: A large database for ai- generated image quality assessment","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.11170","last_updated":"2025-07-27T09:18:49Z","snapshot_observed_at":"2026-08-11T22:10:22.581543Z","submitted_at":"2024-12-15T12:41:44Z","title":"Benchmarking and Learning Multi-Dimensional Quality Evaluator for Text-to-3D Generation","version":2},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-11T15:18:18.552594Z"},"links":{"citing_paper":"/paper/2412.11170"},"observation_digest":"sha256:80afbdcb61126cc2f0ef6ec1c18231b99db54bb40bb525863747cf9b0757b0ac","observation_id":"4df77613-fbc4-4ffa-b538-90263ee53195","resolution":{"observed_at":"2026-08-11T15:18:18.552594Z","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":"2026-08-11T15:18:18.557288Z","title":"Agiqa-3k: An open database for ai-generated image quality assessment","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.11170","last_updated":"2025-07-27T09:18:49Z","snapshot_observed_at":"2026-08-11T22:10:22.581543Z","submitted_at":"2024-12-15T12:41:44Z","title":"Benchmarking and Learning Multi-Dimensional Quality Evaluator for Text-to-3D Generation","version":2},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-11T15:18:18.557288Z"},"links":{"citing_paper":"/paper/2412.11170"},"observation_digest":"sha256:9457cd3c72b36e9d63212177c95d66ba309baf47e54810cbaa4051d567a2088d","observation_id":"adeb4528-d8d8-4757-9439-e53fa9a280cb","resolution":{"observed_at":"2026-08-11T15:18:18.557288Z","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":"2026-08-11T15:18:18.563587Z","title":"Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.11170","last_updated":"2025-07-27T09:18:49Z","snapshot_observed_at":"2026-08-11T22:10:22.581543Z","submitted_at":"2024-12-15T12:41:44Z","title":"Benchmarking and Learning Multi-Dimensional Quality Evaluator for Text-to-3D Generation","version":2},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-11T15:18:18.563587Z"},"links":{"citing_paper":"/paper/2412.11170"},"observation_digest":"sha256:240b6a36eed6b84b94ebd6239c684e68cb85dd824d89b50be9b6f082850daa6f","observation_id":"316d9107-2ff5-405d-a42e-91f55ea8fa7c","resolution":{"observed_at":"2026-08-11T15:18:18.563587Z","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":"2026-08-11T15:18:18.568533Z","title":"Instant3d: Instant text- to-3d generation","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.11170","last_updated":"2025-07-27T09:18:49Z","snapshot_observed_at":"2026-08-11T22:10:22.581543Z","submitted_at":"2024-12-15T12:41:44Z","title":"Benchmarking and Learning Multi-Dimensional Quality Evaluator for Text-to-3D Generation","version":2},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-11T15:18:18.568533Z"},"links":{"citing_paper":"/paper/2412.11170"},"observation_digest":"sha256:d50379d911695fcfe1ee403df48424e9e839dd60c8e601d1abec81bb2576becc","observation_id":"bd2ce643-e37f-4c79-99ad-59df22bddc54","resolution":{"observed_at":"2026-08-11T15:18:18.568533Z","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":"2026-08-11T15:18:18.573589Z","title":"https://threejs.org","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.11170","last_updated":"2025-07-27T09:18:49Z","snapshot_observed_at":"2026-08-11T22:10:22.581543Z","submitted_at":"2024-12-15T12:41:44Z","title":"Benchmarking and Learning Multi-Dimensional Quality Evaluator for Text-to-3D Generation","version":2},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-11T15:18:18.573589Z"},"links":{"citing_paper":"/paper/2412.11170"},"observation_digest":"sha256:2d251a85dd28645b01de8873259270c8e291260945fa80af74b6fc4ca9c8bca1","observation_id":"b149ec03-6efb-4018-9f89-82dec48c4245","resolution":{"observed_at":"2026-08-11T15:18:18.573589Z","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":"2026-08-11T15:18:18.578581Z","title":"Magic3d: High-resolution text-to-3d content creation","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.11170","last_updated":"2025-07-27T09:18:49Z","snapshot_observed_at":"2026-08-11T22:10:22.581543Z","submitted_at":"2024-12-15T12:41:44Z","title":"Benchmarking and Learning Multi-Dimensional Quality Evaluator for Text-to-3D Generation","version":2},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-11T15:18:18.578581Z"},"links":{"citing_paper":"/paper/2412.11170"},"observation_digest":"sha256:b22c14ca4dccd3dcfcd7e1e27ae5c40da10ffff12540985c79e6fda2d91e9b73","observation_id":"7c57c7de-0025-417c-aa77-8694218b324b","resolution":{"observed_at":"2026-08-11T15:18:18.578581Z","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":"2026-08-11T15:18:18.583736Z","title":"Sherpa3d: Boosting high-fidelity text-to-3d genera- tion via coarse 3d prior","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.11170","last_updated":"2025-07-27T09:18:49Z","snapshot_observed_at":"2026-08-11T22:10:22.581543Z","submitted_at":"2024-12-15T12:41:44Z","title":"Benchmarking and Learning Multi-Dimensional Quality Evaluator for Text-to-3D Generation","version":2},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-11T15:18:18.583736Z"},"links":{"citing_paper":"/paper/2412.11170"},"observation_digest":"sha256:9ce38b066ea497633d6188d951fa09eafa501886c70c3e2ef3767ff39f80632d","observation_id":"def72ff0-c56c-4891-b7ff-8f118200efa3","resolution":{"observed_at":"2026-08-11T15:18:18.583736Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.01166","last_updated":"2024-03-19T08:22:42Z","snapshot_observed_at":"2026-08-10T09:23:27.170884Z","submitted_at":"2024-02-02T06:20:44Z","title":"A Comprehensive Survey on 3D Content Generation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.01166","snapshot_observed_at":"2026-08-11T15:18:18.588792Z","title":"A comprehensive survey on 3d con- tent generation","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.11170","last_updated":"2025-07-27T09:18:49Z","snapshot_observed_at":"2026-08-11T22:10:22.581543Z","submitted_at":"2024-12-15T12:41:44Z","title":"Benchmarking and Learning Multi-Dimensional Quality Evaluator for Text-to-3D Generation","version":2},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-11T15:18:18.588792Z"},"links":{"cited_paper":"/paper/2402.01166","citing_paper":"/paper/2412.11170"},"observation_digest":"sha256:b7893756a3fb5e3066f73fc6ecb98c029ec7ed50ac69d895fb60ea4f5ce591e5","observation_id":"b5204bd8-e9da-44fb-87ad-d9e6d522ace6","resolution":{"observed_at":"2026-08-11T15:18:18.588792Z","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":"2026-08-11T15:18:18.593821Z","title":"One-2-3-45++: Fast single image to 3d objects with consistent multi-view generation and 3d diffusion","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.11170","last_updated":"2025-07-27T09:18:49Z","snapshot_observed_at":"2026-08-11T22:10:22.581543Z","submitted_at":"2024-12-15T12:41:44Z","title":"Benchmarking and Learning Multi-Dimensional Quality Evaluator for Text-to-3D Generation","version":2},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-11T15:18:18.593821Z"},"links":{"citing_paper":"/paper/2412.11170"},"observation_digest":"sha256:5651a493f93f9086da0c4a1d24684de85a1ab519d15a00f8ced2f5019596bcf0","observation_id":"f35b5ae5-dd10-47ad-a06e-43b0731c9b95","resolution":{"observed_at":"2026-08-11T15:18:18.593821Z","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":"2026-08-11T15:18:18.598526Z","title":"Zero-1-to-3: Zero-shot one image to 3d object","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.11170","last_updated":"2025-07-27T09:18:49Z","snapshot_observed_at":"2026-08-11T22:10:22.581543Z","submitted_at":"2024-12-15T12:41:44Z","title":"Benchmarking and Learning Multi-Dimensional Quality Evaluator for Text-to-3D Generation","version":2},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-11T15:18:18.598526Z"},"links":{"citing_paper":"/paper/2412.11170"},"observation_digest":"sha256:57e09583810e2d7c0f74e010e3f28ef84e8024aa6141ae01baf8b36ba277343c","observation_id":"9261b58c-3d6c-43a3-862c-8635c3bc9b2e","resolution":{"observed_at":"2026-08-11T15:18:18.598526Z","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":"2026-08-11T15:18:18.604437Z","title":"Vqa-diff: Exploit- ing vqa and diffusion for zero-shot image-to-3d vehicle asset generation in autonomous driving","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.11170","last_updated":"2025-07-27T09:18:49Z","snapshot_observed_at":"2026-08-11T22:10:22.581543Z","submitted_at":"2024-12-15T12:41:44Z","title":"Benchmarking and Learning Multi-Dimensional Quality Evaluator for Text-to-3D Generation","version":2},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-11T15:18:18.604437Z"},"links":{"citing_paper":"/paper/2412.11170"},"observation_digest":"sha256:21ce2cde65ef2aba567a301c5d60c800ec2b51345a567a268adf1451eec723cf","observation_id":"9d408bf0-31a9-4f2f-aa36-ad49aee7594e","resolution":{"observed_at":"2026-08-11T15:18:18.604437Z","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":"2026-08-11T15:18:18.609424Z","title":"Swin transformer: Hierarchical vision transformer using shifted windows","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.11170","last_updated":"2025-07-27T09:18:49Z","snapshot_observed_at":"2026-08-11T22:10:22.581543Z","submitted_at":"2024-12-15T12:41:44Z","title":"Benchmarking and Learning Multi-Dimensional Quality Evaluator for Text-to-3D Generation","version":2},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-11T15:18:18.609424Z"},"links":{"citing_paper":"/paper/2412.11170"},"observation_digest":"sha256:6e18fde213536faaae50ed76017f3fa49dfd0f563bddd63f0f04736da1c3caa7","observation_id":"6ee59c2b-86cc-4a77-a590-7286ccd66dd7","resolution":{"observed_at":"2026-08-11T15:18:18.609424Z","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":"2026-08-11T15:18:18.614285Z","title":"Scaledreamer: Scalable text-to-3d synthesis with asynchronous score distillation","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.11170","last_updated":"2025-07-27T09:18:49Z","snapshot_observed_at":"2026-08-11T22:10:22.581543Z","submitted_at":"2024-12-15T12:41:44Z","title":"Benchmarking and Learning Multi-Dimensional Quality Evaluator for Text-to-3D Generation","version":2},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-11T15:18:18.614285Z"},"links":{"citing_paper":"/paper/2412.11170"},"observation_digest":"sha256:a75412bbee45be838d994c7e990d6b34bdf1aea531417d58811bb2f1d667db94","observation_id":"9f2c525d-3bd8-4d14-b86b-7f313e263127","resolution":{"observed_at":"2026-08-11T15:18:18.614285Z","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":"2026-08-11T15:18:18.619814Z","title":"Latent-nerf for shape-guided generation of 3d shapes and textures","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.11170","last_updated":"2025-07-27T09:18:49Z","snapshot_observed_at":"2026-08-11T22:10:22.581543Z","submitted_at":"2024-12-15T12:41:44Z","title":"Benchmarking and Learning Multi-Dimensional Quality Evaluator for Text-to-3D Generation","version":2},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-11T15:18:18.619814Z"},"links":{"citing_paper":"/paper/2412.11170"},"observation_digest":"sha256:0f3e978089057da05d75d4471064eb81cc1b44d94142df16b27c27ca763126c7","observation_id":"f46ce431-66c8-4d54-9dff-3d5d32a10f57","resolution":{"observed_at":"2026-08-11T15:18:18.619814Z","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":"2026-08-11T15:18:18.624549Z","title":"Nerf: Representing scenes as neural radiance fields for view synthesis","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2412.11170","last_updated":"2025-07-27T09:18:49Z","snapshot_observed_at":"2026-08-11T22:10:22.581543Z","submitted_at":"2024-12-15T12:41:44Z","title":"Benchmarking and Learning Multi-Dimensional Quality Evaluator for Text-to-3D Generation","version":2},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-11T15:18:18.624549Z"},"links":{"citing_paper":"/paper/2412.11170"},"observation_digest":"sha256:4818b3f21eb2a81fff8e764f1d0e72cb1a99162952d0192d41327cd04b524e87","observation_id":"cf32abcd-13a5-4c8c-9c70-1c3751973b79","resolution":{"observed_at":"2026-08-11T15:18:18.624549Z","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":"2026-08-11T15:18:18.629789Z","title":"Clip-mesh: Generating textured meshes from text using pretrained image-text models","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.11170","last_updated":"2025-07-27T09:18:49Z","snapshot_observed_at":"2026-08-11T22:10:22.581543Z","submitted_at":"2024-12-15T12:41:44Z","title":"Benchmarking and Learning Multi-Dimensional Quality Evaluator for Text-to-3D Generation","version":2},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-11T15:18:18.629789Z"},"links":{"citing_paper":"/paper/2412.11170"},"observation_digest":"sha256:b16c040060810364353a33f4e09ba18404a3764b9a37ae055505499e93398709","observation_id":"39a84fa4-71d1-40cd-8529-a66b02ad545b","resolution":{"observed_at":"2026-08-11T15:18:18.629789Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2212.08751","last_updated":"2022-12-16T23:22:59Z","snapshot_observed_at":"2026-07-06T14:31:54.932806Z","submitted_at":"2022-12-16T23:22:59Z","title":"Point-E: A System for Generating 3D Point Clouds from Complex Prompts","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2212.08751","snapshot_observed_at":"2026-08-11T15:18:18.634355Z","title":"Point-e: A system for generat- ing 3d point clouds from complex prompts","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.11170","last_updated":"2025-07-27T09:18:49Z","snapshot_observed_at":"2026-08-11T22:10:22.581543Z","submitted_at":"2024-12-15T12:41:44Z","title":"Benchmarking and Learning Multi-Dimensional Quality Evaluator for Text-to-3D Generation","version":2},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-11T15:18:18.634355Z"},"links":{"cited_paper":"/paper/2212.08751","citing_paper":"/paper/2412.11170"},"observation_digest":"sha256:ad988fe1bb3d555d067e3954ffbb263c8dfc17f5ade3b0e757efccb8e65771d6","observation_id":"107a2279-ac46-47f1-a6ad-eb170d90e434","resolution":{"observed_at":"2026-08-11T15:18:18.634355Z","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":"2026-08-11T15:18:18.639211Z","title":"Gpt-4 system card","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.11170","last_updated":"2025-07-27T09:18:49Z","snapshot_observed_at":"2026-08-11T22:10:22.581543Z","submitted_at":"2024-12-15T12:41:44Z","title":"Benchmarking and Learning Multi-Dimensional Quality Evaluator for Text-to-3D Generation","version":2},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-11T15:18:18.639211Z"},"links":{"citing_paper":"/paper/2412.11170"},"observation_digest":"sha256:a3dae9504dc36f943ebfb6d2739cade0bfc7286d4631b56a62900ab8985970fb","observation_id":"2efdde0b-41b2-4277-9d38-d2f9e550ccbf","resolution":{"observed_at":"2026-08-11T15:18:18.639211Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2304.07193","last_updated":"2024-02-02T10:24:09Z","snapshot_observed_at":"2026-08-11T10:12:11.384939Z","submitted_at":"2023-04-14T15:12:19Z","title":"DINOv2: Learning Robust Visual Features without Supervision","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2304.07193","snapshot_observed_at":"2026-08-11T15:18:18.643917Z","title":"Dinov2: Learning robust visual features without supervision","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.11170","last_updated":"2025-07-27T09:18:49Z","snapshot_observed_at":"2026-08-11T22:10:22.581543Z","submitted_at":"2024-12-15T12:41:44Z","title":"Benchmarking and Learning Multi-Dimensional Quality Evaluator for Text-to-3D Generation","version":2},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-11T15:18:18.643917Z"},"links":{"cited_paper":"/paper/2304.07193","citing_paper":"/paper/2412.11170"},"observation_digest":"sha256:bf5b12e9053562b7abd5d9b02ca8c5e266a6f384ac3771c1b602cf808c59fe59","observation_id":"304a6149-514c-4cf1-848f-77e4fb34f73f","resolution":{"observed_at":"2026-08-11T15:18:18.643917Z","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":"2026-08-11T15:18:18.648868Z","title":"Barron, and Ben Milden- hall","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.11170","last_updated":"2025-07-27T09:18:49Z","snapshot_observed_at":"2026-08-11T22:10:22.581543Z","submitted_at":"2024-12-15T12:41:44Z","title":"Benchmarking and Learning Multi-Dimensional Quality Evaluator for Text-to-3D Generation","version":2},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-11T15:18:18.648868Z"},"links":{"citing_paper":"/paper/2412.11170"},"observation_digest":"sha256:1e6fad9a3bcc5b5bc399c47bf35b5c25da7bf12d168804f108adff34621a0afc","observation_id":"c9a2064c-a8f0-42b8-8737-ff9ab7f0f218","resolution":{"observed_at":"2026-08-11T15:18:18.648868Z","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":"2026-08-11T15:18:18.654309Z","title":"Learn- ing transferable visual models from natural language super- vision","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.11170","last_updated":"2025-07-27T09:18:49Z","snapshot_observed_at":"2026-08-11T22:10:22.581543Z","submitted_at":"2024-12-15T12:41:44Z","title":"Benchmarking and Learning Multi-Dimensional Quality Evaluator for Text-to-3D Generation","version":2},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-11T15:18:18.654309Z"},"links":{"citing_paper":"/paper/2412.11170"},"observation_digest":"sha256:276751534717e434966fc0fc2efdc0e5666fc9cdd0fb54c45229044b30ff484c","observation_id":"6a004737-fb5f-42d4-86ef-e26bf14fa2b2","resolution":{"observed_at":"2026-08-11T15:18:18.654309Z","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":"2026-08-11T15:18:18.659057Z","title":"Methodologies for the subjective assessment of the quality of television images","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.11170","last_updated":"2025-07-27T09:18:49Z","snapshot_observed_at":"2026-08-11T22:10:22.581543Z","submitted_at":"2024-12-15T12:41:44Z","title":"Benchmarking and Learning Multi-Dimensional Quality Evaluator for Text-to-3D Generation","version":2},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-11T15:18:18.659057Z"},"links":{"citing_paper":"/paper/2412.11170"},"observation_digest":"sha256:e55a63b7bb3f47f4e0f70cddaa73608e9b3b89c9173fbcc5647b40a94ba8f8b8","observation_id":"6d2f6499-ea85-46d0-856b-f3474688c195","resolution":{"observed_at":"2026-08-11T15:18:18.659057Z","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":"2026-08-11T15:18:18.664017Z","title":"Subjective video quality as- sessment methods for multimedia applications","venue":null,"work_id":null,"year":1999},"citing_paper":{"arxiv_id":"2412.11170","last_updated":"2025-07-27T09:18:49Z","snapshot_observed_at":"2026-08-11T22:10:22.581543Z","submitted_at":"2024-12-15T12:41:44Z","title":"Benchmarking and Learning Multi-Dimensional Quality Evaluator for Text-to-3D Generation","version":2},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-11T15:18:18.664017Z"},"links":{"citing_paper":"/paper/2412.11170"},"observation_digest":"sha256:d0cf9637e90d57c2d1d5e634f93f32f7958129a42d21edfb080b76cbb5b924ee","observation_id":"f3eebdca-8114-4a29-9e37-2db0b484a2d6","resolution":{"observed_at":"2026-08-11T15:18:18.664017Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2312.17142","last_updated":"2024-06-10T14:07:53Z","snapshot_observed_at":"2026-08-01T13:37:17.913862Z","submitted_at":"2023-12-28T17:16:44Z","title":"DreamGaussian4D: Generative 4D Gaussian Splatting","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2312.17142","snapshot_observed_at":"2026-08-11T15:18:18.670088Z","title":"Dreamgaussian4d: Genera- tive 4d gaussian splatting","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.11170","last_updated":"2025-07-27T09:18:49Z","snapshot_observed_at":"2026-08-11T22:10:22.581543Z","submitted_at":"2024-12-15T12:41:44Z","title":"Benchmarking and Learning Multi-Dimensional Quality Evaluator for Text-to-3D Generation","version":2},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-11T15:18:18.670088Z"},"links":{"cited_paper":"/paper/2312.17142","citing_paper":"/paper/2412.11170"},"observation_digest":"sha256:f67bd887492490a14f09dc550df2af51b96962c9f82300a955b75b7c70618a8c","observation_id":"c0d9947a-01ca-4887-a58f-89c1a1a1c4c1","resolution":{"observed_at":"2026-08-11T15:18:18.670088Z","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":"2026-08-11T15:18:18.674982Z","title":"Laion-5b: An open large-scale dataset for training next generation image-text models","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.11170","last_updated":"2025-07-27T09:18:49Z","snapshot_observed_at":"2026-08-11T22:10:22.581543Z","submitted_at":"2024-12-15T12:41:44Z","title":"Benchmarking and Learning Multi-Dimensional Quality Evaluator for Text-to-3D Generation","version":2},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-11T15:18:18.674982Z"},"links":{"citing_paper":"/paper/2412.11170"},"observation_digest":"sha256:8f8b3c1d4d313bc1229ecfda754f6bf4ba95f0b211653ae491a8a08b3cfbd81b","observation_id":"14c051dc-7892-408f-b0b7-105b704876b0","resolution":{"observed_at":"2026-08-11T15:18:18.674982Z","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":"2026-08-11T15:18:18.680216Z","title":"Retrieval-augmented score distillation for text-to-3d gener- ation","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.11170","last_updated":"2025-07-27T09:18:49Z","snapshot_observed_at":"2026-08-11T22:10:22.581543Z","submitted_at":"2024-12-15T12:41:44Z","title":"Benchmarking and Learning Multi-Dimensional Quality Evaluator for Text-to-3D Generation","version":2},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-11T15:18:18.680216Z"},"links":{"citing_paper":"/paper/2412.11170"},"observation_digest":"sha256:44f0f4b38bc59620a7ffa3b8f2980632a467110cce47507b28e60c56792ba47b","observation_id":"20a21cb5-e9cb-491b-8c63-f709bb06b2f9","resolution":{"observed_at":"2026-08-11T15:18:18.680216Z","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":"2026-08-11T15:18:18.684766Z","title":"Let 2d diffusion model know 3d- consistency for robust text-to-3d generation","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.11170","last_updated":"2025-07-27T09:18:49Z","snapshot_observed_at":"2026-08-11T22:10:22.581543Z","submitted_at":"2024-12-15T12:41:44Z","title":"Benchmarking and Learning Multi-Dimensional Quality Evaluator for Text-to-3D Generation","version":2},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-11T15:18:18.684766Z"},"links":{"citing_paper":"/paper/2412.11170"},"observation_digest":"sha256:753860e60f28d8bd347c63099be006eadc3400a2dbbf50f3d78a00a86eeba5da","observation_id":"b30b077e-2baa-467b-a452-6a740ec05e71","resolution":{"observed_at":"2026-08-11T15:18:18.684766Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2308.16512","last_updated":"2024-04-18T04:12:32Z","snapshot_observed_at":"2026-07-06T16:12:38.269310Z","submitted_at":"2023-08-31T07:49:06Z","title":"MVDream: Multi-view Diffusion for 3D Generation","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2308.16512","snapshot_observed_at":"2026-08-11T15:18:18.689490Z","title":"Mvdream: Multi-view diffusion for 3d gen- eration","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.11170","last_updated":"2025-07-27T09:18:49Z","snapshot_observed_at":"2026-08-11T22:10:22.581543Z","submitted_at":"2024-12-15T12:41:44Z","title":"Benchmarking and Learning Multi-Dimensional Quality Evaluator for Text-to-3D Generation","version":2},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-11T15:18:18.689490Z"},"links":{"cited_paper":"/paper/2308.16512","citing_paper":"/paper/2412.11170"},"observation_digest":"sha256:c3f1ae45966a9cb2e927c9a0c6ac9987e6620650afdeed2b0ca445b2fd381bbb","observation_id":"37d50f8c-0f13-471b-972b-c286393a11e3","resolution":{"observed_at":"2026-08-11T15:18:18.689490Z","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":"2026-08-11T15:18:18.694496Z","title":"Blindly assess image qual- ity in the wild guided by a self-adaptive hyper network","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2412.11170","last_updated":"2025-07-27T09:18:49Z","snapshot_observed_at":"2026-08-11T22:10:22.581543Z","submitted_at":"2024-12-15T12:41:44Z","title":"Benchmarking and Learning Multi-Dimensional Quality Evaluator for Text-to-3D Generation","version":2},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-11T15:18:18.694496Z"},"links":{"citing_paper":"/paper/2412.11170"},"observation_digest":"sha256:84e10085056058859c3153451b1b8cdc0d1955fcd7a17e21026fa0c014ec3e2f","observation_id":"19b09ab6-94d5-4138-8823-def014e89124","resolution":{"observed_at":"2026-08-11T15:18:18.694496Z","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":"2026-08-11T15:18:20.299684Z","title":"Lgm: Large multi-view gaussian model for high-resolution 3d content creation","venue":null,"work_id":"57816733-a4b5-499c-bac1-79be07e8254d","year":2024},"citing_paper":{"arxiv_id":"2412.11170","last_updated":"2025-07-27T09:18:49Z","snapshot_observed_at":"2026-08-11T22:10:22.581543Z","submitted_at":"2024-12-15T12:41:44Z","title":"Benchmarking and Learning Multi-Dimensional Quality Evaluator for Text-to-3D Generation","version":2},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-11T15:18:18.699864Z"},"links":{"citing_paper":"/paper/2412.11170"},"observation_digest":"sha256:11944ab5470dbc64aab08fb474109b32806cc90625dd76919991fbffa939be91","observation_id":"2723af77-4465-4342-a014-0b4a2ec1feee","resolution":{"observed_at":"2026-08-11T15:18:20.305723Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-11T15:18:20.283981Z","title":"Dreamgaussian: Generative gaussian splatting for ef- ficient 3d content creation","venue":null,"work_id":"15a0afa6-caf2-45da-872b-28a127bc3116","year":2024},"citing_paper":{"arxiv_id":"2412.11170","last_updated":"2025-07-27T09:18:49Z","snapshot_observed_at":"2026-08-11T22:10:22.581543Z","submitted_at":"2024-12-15T12:41:44Z","title":"Benchmarking and Learning Multi-Dimensional Quality Evaluator for Text-to-3D Generation","version":2},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-11T15:18:18.704602Z"},"links":{"citing_paper":"/paper/2412.11170"},"observation_digest":"sha256:efbc748eeea5becc26c31b63747f20e37106fc2b53fb7b61871f61e8e1e75c8a","observation_id":"52234dbe-5024-47f2-ba93-211457ae194d","resolution":{"observed_at":"2026-08-11T15:18:20.289007Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-11T15:18:20.268565Z","title":"Textmesh: Gen- eration of realistic 3d meshes from text prompts","venue":null,"work_id":"93986854-2069-48d6-8fb7-d0f79e8d8572","year":2024},"citing_paper":{"arxiv_id":"2412.11170","last_updated":"2025-07-27T09:18:49Z","snapshot_observed_at":"2026-08-11T22:10:22.581543Z","submitted_at":"2024-12-15T12:41:44Z","title":"Benchmarking and Learning Multi-Dimensional Quality Evaluator for Text-to-3D Generation","version":2},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-11T15:18:18.709488Z"},"links":{"citing_paper":"/paper/2412.11170"},"observation_digest":"sha256:9ab1f8e86d9ec6821e6830fb239602df397c04705dfc8275260172683325f746","observation_id":"017b97cf-44d3-4c62-8eef-b9292f6abb91","resolution":{"observed_at":"2026-08-11T15:18:20.273475Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-11T15:18:20.253388Z","title":"Yeh, and Gregory Shakhnarovich","venue":null,"work_id":"d29a0565-825e-4ed3-8842-4776dbb9302c","year":2023},"citing_paper":{"arxiv_id":"2412.11170","last_updated":"2025-07-27T09:18:49Z","snapshot_observed_at":"2026-08-11T22:10:22.581543Z","submitted_at":"2024-12-15T12:41:44Z","title":"Benchmarking and Learning Multi-Dimensional Quality Evaluator for Text-to-3D Generation","version":2},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-11T15:18:18.714069Z"},"links":{"citing_paper":"/paper/2412.11170"},"observation_digest":"sha256:fcfc472bb67f44ded682a653654607cad5f687f66a1bc19d0c42e1f4818b6a68","observation_id":"8e114f75-803f-47b7-84d3-ffec6b26db6e","resolution":{"observed_at":"2026-08-11T15:18:20.258337Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-11T15:18:20.237613Z","title":"Ex- ploring clip for assessing the look and feel of images","venue":null,"work_id":"20f0081a-edd2-42a4-a67e-a28118e6cf7f","year":2023},"citing_paper":{"arxiv_id":"2412.11170","last_updated":"2025-07-27T09:18:49Z","snapshot_observed_at":"2026-08-11T22:10:22.581543Z","submitted_at":"2024-12-15T12:41:44Z","title":"Benchmarking and Learning Multi-Dimensional Quality Evaluator for Text-to-3D Generation","version":2},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-11T15:18:18.718736Z"},"links":{"citing_paper":"/paper/2412.11170"},"observation_digest":"sha256:8473c8b207290aeca7f8ba64bd7eeb01ca55782be70f4a02a1326239969ad5d6","observation_id":"f472b3e6-05ae-418e-b5fa-f4ab39dfec78","resolution":{"observed_at":"2026-08-11T15:18:20.242613Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-11T15:18:20.220897Z","title":"Prolificdreamer: high-fidelity and diverse text-to-3d generation with variational score distilla- tion","venue":null,"work_id":"5e261876-4ff9-42da-b1b1-411f7296883f","year":2024},"citing_paper":{"arxiv_id":"2412.11170","last_updated":"2025-07-27T09:18:49Z","snapshot_observed_at":"2026-08-11T22:10:22.581543Z","submitted_at":"2024-12-15T12:41:44Z","title":"Benchmarking and Learning Multi-Dimensional Quality Evaluator for Text-to-3D Generation","version":2},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-11T15:18:18.723495Z"},"links":{"citing_paper":"/paper/2412.11170"},"observation_digest":"sha256:d37cf096997c14ac25fdf649403fe5ef159455c3409c047de1569540100a5760","observation_id":"41fbb1cd-d6ff-415a-b35a-807a6893f2d4","resolution":{"observed_at":"2026-08-11T15:18:20.226371Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2312.17090","last_updated":"2023-12-28T16:10:25Z","snapshot_observed_at":"2026-08-02T07:14:02.308302Z","submitted_at":"2023-12-28T16:10:25Z","title":"Q-Align: Teaching LMMs for Visual Scoring via Discrete Text-Defined Levels","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2312.17090","snapshot_observed_at":"2026-08-11T15:18:18.728236Z","title":"Q-align: Teaching lmms for visual scoring via discrete text-defined levels","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.11170","last_updated":"2025-07-27T09:18:49Z","snapshot_observed_at":"2026-08-11T22:10:22.581543Z","submitted_at":"2024-12-15T12:41:44Z","title":"Benchmarking and Learning Multi-Dimensional Quality Evaluator for Text-to-3D Generation","version":2},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-11T15:18:18.728236Z"},"links":{"cited_paper":"/paper/2312.17090","citing_paper":"/paper/2412.11170"},"observation_digest":"sha256:510167e1f41fe6785e9b293eeac292ac8a27347bd9f9a16c8e5288b6492f2c07","observation_id":"79e6547d-3c2a-4306-81fd-2cf9695da951","resolution":{"observed_at":"2026-08-11T15:18:18.728236Z","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":"2026-08-11T15:18:20.204148Z","title":"Gpt- 4v(ision) is a human-aligned evaluator for text-to-3d gener- ation","venue":null,"work_id":"52c54719-24b2-4b45-8780-28e5fa83e086","year":2024},"citing_paper":{"arxiv_id":"2412.11170","last_updated":"2025-07-27T09:18:49Z","snapshot_observed_at":"2026-08-11T22:10:22.581543Z","submitted_at":"2024-12-15T12:41:44Z","title":"Benchmarking and Learning Multi-Dimensional Quality Evaluator for Text-to-3D Generation","version":2},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-11T15:18:18.733321Z"},"links":{"citing_paper":"/paper/2412.11170"},"observation_digest":"sha256:20f70edc6c9dca52933e043fe1a510af9471bf7c38ec2d3f6e03ed8a21f3cefe","observation_id":"b4c32e13-d088-446c-9aaf-1a92534d563e","resolution":{"observed_at":"2026-08-11T15:18:20.210000Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2306.09341","last_updated":"2023-09-25T08:19:23Z","snapshot_observed_at":"2026-08-11T00:30:13.593181Z","submitted_at":"2023-06-15T17:59:31Z","title":"Human Preference Score v2: A Solid Benchmark for Evaluating Human Preferences of Text-to-Image Synthesis","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2306.09341","snapshot_observed_at":"2026-08-11T15:18:18.738159Z","title":"Human preference score v2: A solid benchmark for evaluating human preferences of text-to-image synthesis","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.11170","last_updated":"2025-07-27T09:18:49Z","snapshot_observed_at":"2026-08-11T22:10:22.581543Z","submitted_at":"2024-12-15T12:41:44Z","title":"Benchmarking and Learning Multi-Dimensional Quality Evaluator for Text-to-3D Generation","version":2},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-11T15:18:18.738159Z"},"links":{"cited_paper":"/paper/2306.09341","citing_paper":"/paper/2412.11170"},"observation_digest":"sha256:456bb4edbff2755e48cd8db7a7b30e04edb171842395874f26e84068ba49c9f9","observation_id":"f80854bf-7750-4991-bf04-f45fda1b2c45","resolution":{"observed_at":"2026-08-11T15:18:18.738159Z","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":"2026-08-11T15:18:20.188733Z","title":"Consistent3d: Towards consistent high-fidelity text-to-3d generation with deterministic sampling prior","venue":null,"work_id":"30b2384e-7dc8-47db-aa95-2f6565e3ebbb","year":2024},"citing_paper":{"arxiv_id":"2412.11170","last_updated":"2025-07-27T09:18:49Z","snapshot_observed_at":"2026-08-11T22:10:22.581543Z","submitted_at":"2024-12-15T12:41:44Z","title":"Benchmarking and Learning Multi-Dimensional Quality Evaluator for Text-to-3D Generation","version":2},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-08-11T15:18:18.743148Z"},"links":{"citing_paper":"/paper/2412.11170"},"observation_digest":"sha256:deabfd91f31f041c24acb85161ad5a7a52aee29529d69028c90dd1f94bd6413b","observation_id":"88dccfaf-54da-4975-b61e-d2f053ca475b","resolution":{"observed_at":"2026-08-11T15:18:20.193649Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-11T15:18:20.172718Z","title":"Dream3d: Zero-shot text-to-3d synthesis using 3d shape prior and text-to-image diffusion models","venue":null,"work_id":"5fbf66fd-7eaf-4722-ba97-cc9b7b300fff","year":2023},"citing_paper":{"arxiv_id":"2412.11170","last_updated":"2025-07-27T09:18:49Z","snapshot_observed_at":"2026-08-11T22:10:22.581543Z","submitted_at":"2024-12-15T12:41:44Z","title":"Benchmarking and Learning Multi-Dimensional Quality Evaluator for Text-to-3D Generation","version":2},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-08-11T15:18:18.748104Z"},"links":{"citing_paper":"/paper/2412.11170"},"observation_digest":"sha256:71a4c8a7fb1ae639dad359672b4e56b16c03a5ca5b0aa92deb7d78e47ce75947","observation_id":"64e45150-04a1-4c6d-bb5f-819216e2c772","resolution":{"observed_at":"2026-08-11T15:18:20.178024Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-11T15:18:20.155692Z","title":"Imagere- ward: Learning and evaluating human preferences for text- to-image generation","venue":null,"work_id":"52621bd9-8675-4967-a7db-ffdada156069","year":2024},"citing_paper":{"arxiv_id":"2412.11170","last_updated":"2025-07-27T09:18:49Z","snapshot_observed_at":"2026-08-11T22:10:22.581543Z","submitted_at":"2024-12-15T12:41:44Z","title":"Benchmarking and Learning Multi-Dimensional Quality Evaluator for Text-to-3D Generation","version":2},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-08-11T15:18:18.752865Z"},"links":{"citing_paper":"/paper/2412.11170"},"observation_digest":"sha256:fc21292baab444ea498a9dd59e6f8e57e1310257e9c3176cb8469d6134f25556","observation_id":"d7731484-7120-4a2b-80b6-62113279e4ed","resolution":{"observed_at":"2026-08-11T15:18:20.161552Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-11T15:18:20.139171Z","title":"Dreamview: Inject- ing view-specific text guidance into text-to-3d generation","venue":null,"work_id":"6436babf-b6fa-4272-b57f-b24284fba762","year":2024},"citing_paper":{"arxiv_id":"2412.11170","last_updated":"2025-07-27T09:18:49Z","snapshot_observed_at":"2026-08-11T22:10:22.581543Z","submitted_at":"2024-12-15T12:41:44Z","title":"Benchmarking and Learning Multi-Dimensional Quality Evaluator for Text-to-3D Generation","version":2},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-08-11T15:18:18.757623Z"},"links":{"citing_paper":"/paper/2412.11170"},"observation_digest":"sha256:d521d087defbcc57a3944b650ef8d21b55999c5211c274d61de3aa620c9d6830","observation_id":"e305ae82-43bd-4705-a329-f88e513f1bac","resolution":{"observed_at":"2026-08-11T15:18:20.144849Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-11T15:18:20.123032Z","title":"Consistent flow distillation for text-to-3d generation","venue":null,"work_id":"b492a7da-fbd8-4fd8-839b-aa5d8c0f3cf5","year":2025},"citing_paper":{"arxiv_id":"2412.11170","last_updated":"2025-07-27T09:18:49Z","snapshot_observed_at":"2026-08-11T22:10:22.581543Z","submitted_at":"2024-12-15T12:41:44Z","title":"Benchmarking and Learning Multi-Dimensional Quality Evaluator for Text-to-3D Generation","version":2},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-08-11T15:18:18.762272Z"},"links":{"citing_paper":"/paper/2412.11170"},"observation_digest":"sha256:9004c2a6b547e24ef155ea25efd6a42bb1dc90072f9857744022edeed7e97714","observation_id":"1e10033d-db66-490b-b304-f53660bf95de","resolution":{"observed_at":"2026-08-11T15:18:20.128340Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-11T15:18:20.106219Z","title":"Viewfusion: Towards multi-view consistency via interpolated denoising","venue":null,"work_id":"7f72eac8-aee2-479f-94d1-2d7f9ff98800","year":2024},"citing_paper":{"arxiv_id":"2412.11170","last_updated":"2025-07-27T09:18:49Z","snapshot_observed_at":"2026-08-11T22:10:22.581543Z","submitted_at":"2024-12-15T12:41:44Z","title":"Benchmarking and Learning Multi-Dimensional Quality Evaluator for Text-to-3D Generation","version":2},"reference_index":71,"source":"pdf_text","source_observed_at":"2026-08-11T15:18:18.766902Z"},"links":{"citing_paper":"/paper/2412.11170"},"observation_digest":"sha256:d46e5940799d44dab0d4d4858e34431f2bfcb754e18143eb148d708521ceba29","observation_id":"28521ee0-1996-4fd9-aaed-2615ac56f58f","resolution":{"observed_at":"2026-08-11T15:18:20.111324Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-11T15:18:20.090296Z","title":"Dream- reward: Text-to-3d generation with human preference","venue":null,"work_id":"cbee0412-9a59-461e-9d2d-a878f4b4c143","year":2024},"citing_paper":{"arxiv_id":"2412.11170","last_updated":"2025-07-27T09:18:49Z","snapshot_observed_at":"2026-08-11T22:10:22.581543Z","submitted_at":"2024-12-15T12:41:44Z","title":"Benchmarking and Learning Multi-Dimensional Quality Evaluator for Text-to-3D Generation","version":2},"reference_index":72,"source":"pdf_text","source_observed_at":"2026-08-11T15:18:18.771509Z"},"links":{"citing_paper":"/paper/2412.11170"},"observation_digest":"sha256:c2f4face4d473aed3152b8484072d5ce2acd76aea6878598db9dcb7ac4a532a1","observation_id":"ad076210-2462-4afa-bd7e-6f6f5e9f46a3","resolution":{"observed_at":"2026-08-11T15:18:20.095412Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-11T15:18:20.074907Z","title":"Consistent-1-to-3: Consistent image to 3d view syn- thesis via geometry-aware diffusion models","venue":null,"work_id":"f52805b9-59fd-4317-9949-e7adf6ea9d2d","year":2024},"citing_paper":{"arxiv_id":"2412.11170","last_updated":"2025-07-27T09:18:49Z","snapshot_observed_at":"2026-08-11T22:10:22.581543Z","submitted_at":"2024-12-15T12:41:44Z","title":"Benchmarking and Learning Multi-Dimensional Quality Evaluator for Text-to-3D Generation","version":2},"reference_index":73,"source":"pdf_text","source_observed_at":"2026-08-11T15:18:18.776046Z"},"links":{"citing_paper":"/paper/2412.11170"},"observation_digest":"sha256:00c9ba63e836e144193ccce20a72ec1a83ae5e951f5d77382fb84a28b4983873","observation_id":"47c8388b-b27b-46c6-88e2-17d041bbd3a4","resolution":{"observed_at":"2026-08-11T15:18:20.080256Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-11T15:18:20.059378Z","title":"Gaussiandreamer: Fast generation from text to 3d gaussians by bridging 2d and 3d diffusion models","venue":null,"work_id":"e15006da-e3d7-46c1-a924-c5a117855308","year":2024},"citing_paper":{"arxiv_id":"2412.11170","last_updated":"2025-07-27T09:18:49Z","snapshot_observed_at":"2026-08-11T22:10:22.581543Z","submitted_at":"2024-12-15T12:41:44Z","title":"Benchmarking and Learning Multi-Dimensional Quality Evaluator for Text-to-3D Generation","version":2},"reference_index":74,"source":"pdf_text","source_observed_at":"2026-08-11T15:18:18.780849Z"},"links":{"citing_paper":"/paper/2412.11170"},"observation_digest":"sha256:ed99569339acc83b86e08777e3192a4634d4825166778979aa7c8b4e6173e131","observation_id":"2f800c9a-4d9d-4dfb-a4d7-94b2a4b10095","resolution":{"observed_at":"2026-08-11T15:18:20.064673Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-11T15:18:20.042187Z","title":"Gaussiandreamerpro: Text to manipula- ble 3d gaussians with highly enhanced quality","venue":null,"work_id":"3692ab2c-4f06-497e-a383-25fb345f0f41","year":2024},"citing_paper":{"arxiv_id":"2412.11170","last_updated":"2025-07-27T09:18:49Z","snapshot_observed_at":"2026-08-11T22:10:22.581543Z","submitted_at":"2024-12-15T12:41:44Z","title":"Benchmarking and Learning Multi-Dimensional Quality Evaluator for Text-to-3D Generation","version":2},"reference_index":75,"source":"pdf_text","source_observed_at":"2026-08-11T15:18:18.785963Z"},"links":{"citing_paper":"/paper/2412.11170"},"observation_digest":"sha256:314948c9ec4de62a511a7bc8e70d1e9b6e8adce5cac7674bec20ecabedaaf8d7","observation_id":"9b6e70b7-a05e-413e-b0cf-6d0d8175a142","resolution":{"observed_at":"2026-08-11T15:18:20.048022Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-11T15:18:20.024559Z","title":"3dshape2vecset: A 3d shape representation for neu- ral fields and generative diffusion models.ACM Transactions On Graphics (TOG), 2023","venue":null,"work_id":"402cbedd-410a-45db-b2ff-dfc77bdcbe39","year":2023},"citing_paper":{"arxiv_id":"2412.11170","last_updated":"2025-07-27T09:18:49Z","snapshot_observed_at":"2026-08-11T22:10:22.581543Z","submitted_at":"2024-12-15T12:41:44Z","title":"Benchmarking and Learning Multi-Dimensional Quality Evaluator for Text-to-3D Generation","version":2},"reference_index":76,"source":"pdf_text","source_observed_at":"2026-08-11T15:18:18.791032Z"},"links":{"citing_paper":"/paper/2412.11170"},"observation_digest":"sha256:75c4c0d5b9b49d8d1c1e564bbcfa958d8c9b4336256e0eeaf7df3e3746a255a4","observation_id":"5a3cb29e-67f4-45f7-b027-cf5ef42bf41c","resolution":{"observed_at":"2026-08-11T15:18:20.030459Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-11T15:18:20.007557Z","title":"Clay: A controllable large-scale generative model for creat- ing high-quality 3d assets","venue":null,"work_id":"b9cd1dac-9cc2-4978-aa14-b9dc5f1a0af6","year":2024},"citing_paper":{"arxiv_id":"2412.11170","last_updated":"2025-07-27T09:18:49Z","snapshot_observed_at":"2026-08-11T22:10:22.581543Z","submitted_at":"2024-12-15T12:41:44Z","title":"Benchmarking and Learning Multi-Dimensional Quality Evaluator for Text-to-3D Generation","version":2},"reference_index":77,"source":"pdf_text","source_observed_at":"2026-08-11T15:18:18.795512Z"},"links":{"citing_paper":"/paper/2412.11170"},"observation_digest":"sha256:de20571b13f61dc738de1283d8eb2079285b1d3d395f97b224996838e8a7a5c6","observation_id":"335ad0af-d7ab-4a92-afd4-8445c8b3666c","resolution":{"observed_at":"2026-08-11T15:18:20.012723Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-11T15:18:19.990737Z","title":"Learning multi- dimensional human preference for text-to-image generation","venue":null,"work_id":"df9f8b4a-3a65-4704-be86-a1f46bf0cca4","year":2024},"citing_paper":{"arxiv_id":"2412.11170","last_updated":"2025-07-27T09:18:49Z","snapshot_observed_at":"2026-08-11T22:10:22.581543Z","submitted_at":"2024-12-15T12:41:44Z","title":"Benchmarking and Learning Multi-Dimensional Quality Evaluator for Text-to-3D Generation","version":2},"reference_index":78,"source":"pdf_text","source_observed_at":"2026-08-11T15:18:18.800049Z"},"links":{"citing_paper":"/paper/2412.11170"},"observation_digest":"sha256:f574fde0936d25cecc2ff57f2b1ac526041935531936c6a686d034ec7bda6da8","observation_id":"68238212-70c4-445c-bc26-a5ed99007274","resolution":{"observed_at":"2026-08-11T15:18:19.996017Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-11T15:18:19.974549Z","title":"Learning to prompt for vision-language models","venue":null,"work_id":"da624988-8de0-4f15-8ada-be310a6ef3b6","year":2022},"citing_paper":{"arxiv_id":"2412.11170","last_updated":"2025-07-27T09:18:49Z","snapshot_observed_at":"2026-08-11T22:10:22.581543Z","submitted_at":"2024-12-15T12:41:44Z","title":"Benchmarking and Learning Multi-Dimensional Quality Evaluator for Text-to-3D Generation","version":2},"reference_index":79,"source":"pdf_text","source_observed_at":"2026-08-11T15:18:18.804605Z"},"links":{"citing_paper":"/paper/2412.11170"},"observation_digest":"sha256:e442e814fea9d31470bddb7be2af49c074e6bbd38f0ae63890671ef4d817daf2","observation_id":"210fedad-3dd0-4aed-9695-0cdc9868a12c","resolution":{"observed_at":"2026-08-11T15:18:19.979692Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-11T15:18:19.959184Z","title":"A green apple","venue":null,"work_id":"cab152cb-c8c0-4b26-a956-74b9827af5aa","year":2024},"citing_paper":{"arxiv_id":"2412.11170","last_updated":"2025-07-27T09:18:49Z","snapshot_observed_at":"2026-08-11T22:10:22.581543Z","submitted_at":"2024-12-15T12:41:44Z","title":"Benchmarking and Learning Multi-Dimensional Quality Evaluator for Text-to-3D Generation","version":2},"reference_index":80,"source":"pdf_text","source_observed_at":"2026-08-11T15:18:18.809430Z"},"links":{"citing_paper":"/paper/2412.11170"},"observation_digest":"sha256:1d278020e4c1504c5d51c4b9a7c78cae30a06589c80e73323b1b0f25394877f5","observation_id":"934e70d7-1afb-4c49-8ba3-4f422731f807","resolution":{"observed_at":"2026-08-11T15:18:19.963918Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-11T15:18:19.943143Z","title":null,"venue":null,"work_id":"152c3813-ec9d-4475-8fd1-9dec5e604b37","year":null},"citing_paper":{"arxiv_id":"2412.11170","last_updated":"2025-07-27T09:18:49Z","snapshot_observed_at":"2026-08-11T22:10:22.581543Z","submitted_at":"2024-12-15T12:41:44Z","title":"Benchmarking and Learning Multi-Dimensional Quality Evaluator for Text-to-3D Generation","version":2},"reference_index":81,"source":"pdf_text","source_observed_at":"2026-08-11T15:18:18.815926Z"},"links":{"citing_paper":"/paper/2412.11170"},"observation_digest":"sha256:7c47382288f41b36db7b203456917b999705ddccf20b636c3c43c5ed87195e8d","observation_id":"a821c771-7f50-41bd-b9c7-d95b1e1f3409","resolution":{"observed_at":"2026-08-11T15:18:19.948276Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-11T15:18:19.927858Z","title":"- Symmetry: Define if the object should be symmetrical, asymmetrical, or radially symmetrical","venue":null,"work_id":"7b6525d2-c083-4402-93c1-adecf54d2c79","year":null},"citing_paper":{"arxiv_id":"2412.11170","last_updated":"2025-07-27T09:18:49Z","snapshot_observed_at":"2026-08-11T22:10:22.581543Z","submitted_at":"2024-12-15T12:41:44Z","title":"Benchmarking and Learning Multi-Dimensional Quality Evaluator for Text-to-3D Generation","version":2},"reference_index":82,"source":"pdf_text","source_observed_at":"2026-08-11T15:18:18.820817Z"},"links":{"citing_paper":"/paper/2412.11170"},"observation_digest":"sha256:7d7e04f09a8b01c8a0b9afef481a57181ed7f039c6fdc0da4e048cedf159293e","observation_id":"7d5e93c8-7820-46fc-ae2b-d451a43b04f6","resolution":{"observed_at":"2026-08-11T15:18:19.932740Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-11T15:18:19.911896Z","title":null,"venue":null,"work_id":"6c6c94d0-939e-4a4c-b306-308f153a8a0e","year":null},"citing_paper":{"arxiv_id":"2412.11170","last_updated":"2025-07-27T09:18:49Z","snapshot_observed_at":"2026-08-11T22:10:22.581543Z","submitted_at":"2024-12-15T12:41:44Z","title":"Benchmarking and Learning Multi-Dimensional Quality Evaluator for Text-to-3D Generation","version":2},"reference_index":83,"source":"pdf_text","source_observed_at":"2026-08-11T15:18:18.826491Z"},"links":{"citing_paper":"/paper/2412.11170"},"observation_digest":"sha256:66f249f1f547869039287ced1e2dd298aa028ef2dc7821d6c70ff7ab988b9a80","observation_id":"210632bc-9f04-4fd1-b5fd-c8246f170a6f","resolution":{"observed_at":"2026-08-11T15:18:19.917456Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-11T15:18:19.897284Z","title":"A green apple","venue":null,"work_id":"016d017b-8203-4f55-996c-b3f7687cac3f","year":null},"citing_paper":{"arxiv_id":"2412.11170","last_updated":"2025-07-27T09:18:49Z","snapshot_observed_at":"2026-08-11T22:10:22.581543Z","submitted_at":"2024-12-15T12:41:44Z","title":"Benchmarking and Learning Multi-Dimensional Quality Evaluator for Text-to-3D Generation","version":2},"reference_index":84,"source":"pdf_text","source_observed_at":"2026-08-11T15:18:18.830983Z"},"links":{"citing_paper":"/paper/2412.11170"},"observation_digest":"sha256:3399f38f4c7946ba6be65a6d6fbd3787fcfc427be9233a7bfe036782802fdfd2","observation_id":"dee03900-bb4f-405b-8d31-8c41ca822a3d","resolution":{"observed_at":"2026-08-11T15:18:19.901856Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-11T15:18:19.882113Z","title":"A photo of a black bird","venue":null,"work_id":"2b693490-8c6b-4aba-9e84-864f03d2dc2c","year":null},"citing_paper":{"arxiv_id":"2412.11170","last_updated":"2025-07-27T09:18:49Z","snapshot_observed_at":"2026-08-11T22:10:22.581543Z","submitted_at":"2024-12-15T12:41:44Z","title":"Benchmarking and Learning Multi-Dimensional Quality Evaluator for Text-to-3D Generation","version":2},"reference_index":85,"source":"pdf_text","source_observed_at":"2026-08-11T15:18:18.835538Z"},"links":{"citing_paper":"/paper/2412.11170"},"observation_digest":"sha256:817682c364ebb880b8d6c275b76d308add5db05ac96753a53ef1f1a8485676c0","observation_id":"6ad42ec7-aa97-40ec-971c-e7d380c9a9cf","resolution":{"observed_at":"2026-08-11T15:18:19.887359Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-11T15:18:19.867261Z","title":"A brown teddy bear, fur matted, one eye missing","venue":null,"work_id":"186c8642-fbf6-499a-9d4f-d0f8b255425d","year":null},"citing_paper":{"arxiv_id":"2412.11170","last_updated":"2025-07-27T09:18:49Z","snapshot_observed_at":"2026-08-11T22:10:22.581543Z","submitted_at":"2024-12-15T12:41:44Z","title":"Benchmarking and Learning Multi-Dimensional Quality Evaluator for Text-to-3D Generation","version":2},"reference_index":86,"source":"pdf_text","source_observed_at":"2026-08-11T15:18:18.839917Z"},"links":{"citing_paper":"/paper/2412.11170"},"observation_digest":"sha256:11e2ef2148753c6af47e809476bb1b13c17687dc4fe0cb7627a2396451f88631","observation_id":"e62849c6-2640-4799-8310-8580d69268c1","resolution":{"observed_at":"2026-08-11T15:18:19.871964Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-11T15:18:19.852361Z","title":"A frog with a translucent skin displaying a mechanical heart beating","venue":null,"work_id":"93b65e4c-827c-45d4-8f2f-bde120718e83","year":null},"citing_paper":{"arxiv_id":"2412.11170","last_updated":"2025-07-27T09:18:49Z","snapshot_observed_at":"2026-08-11T22:10:22.581543Z","submitted_at":"2024-12-15T12:41:44Z","title":"Benchmarking and Learning Multi-Dimensional Quality Evaluator for Text-to-3D Generation","version":2},"reference_index":87,"source":"pdf_text","source_observed_at":"2026-08-11T15:18:18.844416Z"},"links":{"citing_paper":"/paper/2412.11170"},"observation_digest":"sha256:17ae62ef3c20824f2065b02cdc23b3fee3b35f39ad963baa7fd3bf357dabe76e","observation_id":"7a5a56d0-9070-4a5a-896f-efe447299f1a","resolution":{"observed_at":"2026-08-11T15:18:19.856917Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-11T15:18:19.837738Z","title":"A delicious hamburger and a green apple","venue":null,"work_id":"0d2e98c6-529e-4fea-90cb-530eb18d7ef3","year":null},"citing_paper":{"arxiv_id":"2412.11170","last_updated":"2025-07-27T09:18:49Z","snapshot_observed_at":"2026-08-11T22:10:22.581543Z","submitted_at":"2024-12-15T12:41:44Z","title":"Benchmarking and Learning Multi-Dimensional Quality Evaluator for Text-to-3D Generation","version":2},"reference_index":88,"source":"pdf_text","source_observed_at":"2026-08-11T15:18:18.849771Z"},"links":{"citing_paper":"/paper/2412.11170"},"observation_digest":"sha256:e4c746878c2dc8e2f05990fe0c7013972968fcae0ac6efb9e3c9002d613af7ea","observation_id":"66dee391-51e1-401b-a9fe-c455e765d9d3","resolution":{"observed_at":"2026-08-11T15:18:19.842504Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-11T15:18:19.822505Z","title":"A humanoid robot with a top hat is playing the cello","venue":null,"work_id":"dc5b87bd-c783-406d-96a8-9e3cbebec568","year":null},"citing_paper":{"arxiv_id":"2412.11170","last_updated":"2025-07-27T09:18:49Z","snapshot_observed_at":"2026-08-11T22:10:22.581543Z","submitted_at":"2024-12-15T12:41:44Z","title":"Benchmarking and Learning Multi-Dimensional Quality Evaluator for Text-to-3D Generation","version":2},"reference_index":89,"source":"pdf_text","source_observed_at":"2026-08-11T15:18:18.854927Z"},"links":{"citing_paper":"/paper/2412.11170"},"observation_digest":"sha256:8db023f47c471cbf31b8d1fe6fc63b6b6c7373eed0d0eecbcf30e753beadb409","observation_id":"02f335d6-95ce-4591-9be6-1a4f4830298e","resolution":{"observed_at":"2026-08-11T15:18:19.827673Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-11T15:18:19.807902Z","title":"A red apple on a round ceramic plate","venue":null,"work_id":"cb11505e-db12-4075-bed2-885006c4db1c","year":null},"citing_paper":{"arxiv_id":"2412.11170","last_updated":"2025-07-27T09:18:49Z","snapshot_observed_at":"2026-08-11T22:10:22.581543Z","submitted_at":"2024-12-15T12:41:44Z","title":"Benchmarking and Learning Multi-Dimensional Quality Evaluator for Text-to-3D Generation","version":2},"reference_index":90,"source":"pdf_text","source_observed_at":"2026-08-11T15:18:18.859531Z"},"links":{"citing_paper":"/paper/2412.11170"},"observation_digest":"sha256:322ddd5446708d868f26dd9f9c3de94b883ccebe5d4a9a4a56513f00b81807be","observation_id":"ec9ecd8a-9158-4cf1-b954-a50ac366420f","resolution":{"observed_at":"2026-08-11T15:18:19.812688Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-11T15:18:19.790852Z","title":"A panda with a wizard hat is reading a newspaper","venue":null,"work_id":"2234b149-1a08-4aac-8f2c-f54f2fd873bd","year":null},"citing_paper":{"arxiv_id":"2412.11170","last_updated":"2025-07-27T09:18:49Z","snapshot_observed_at":"2026-08-11T22:10:22.581543Z","submitted_at":"2024-12-15T12:41:44Z","title":"Benchmarking and Learning Multi-Dimensional Quality Evaluator for Text-to-3D Generation","version":2},"reference_index":91,"source":"pdf_text","source_observed_at":"2026-08-11T15:18:18.864257Z"},"links":{"citing_paper":"/paper/2412.11170"},"observation_digest":"sha256:ab0c9e6f1722a3384a63ccd41f32210c6459b5011896e472f88bb3fa492166d1","observation_id":"cf880961-4a21-4496-9a87-a3dcdb564053","resolution":{"observed_at":"2026-08-11T15:18:19.796615Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-11T15:18:19.775740Z","title":null,"venue":null,"work_id":"f2953af6-986d-451d-a18e-b1612f4fca5b","year":null},"citing_paper":{"arxiv_id":"2412.11170","last_updated":"2025-07-27T09:18:49Z","snapshot_observed_at":"2026-08-11T22:10:22.581543Z","submitted_at":"2024-12-15T12:41:44Z","title":"Benchmarking and Learning Multi-Dimensional Quality Evaluator for Text-to-3D Generation","version":2},"reference_index":92,"source":"pdf_text","source_observed_at":"2026-08-11T15:18:18.868965Z"},"links":{"citing_paper":"/paper/2412.11170"},"observation_digest":"sha256:30b7b1bd55552061c7d7a1297f450614a1458d0550ecb0eac45ca442e2fd7197","observation_id":"0ef7621e-ebb2-4a47-a177-71efdc553e49","resolution":{"observed_at":"2026-08-11T15:18:19.780482Z","resolver_source":"raw_fallback","status":"parse_uncertain"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-11T15:18:19.760969Z","title":null,"venue":null,"work_id":"fb5f72af-3703-42aa-993c-12b0a249115c","year":null},"citing_paper":{"arxiv_id":"2412.11170","last_updated":"2025-07-27T09:18:49Z","snapshot_observed_at":"2026-08-11T22:10:22.581543Z","submitted_at":"2024-12-15T12:41:44Z","title":"Benchmarking and Learning Multi-Dimensional Quality Evaluator for Text-to-3D Generation","version":2},"reference_index":93,"source":"pdf_text","source_observed_at":"2026-08-11T15:18:18.873380Z"},"links":{"citing_paper":"/paper/2412.11170"},"observation_digest":"sha256:d91e9a47a82f6be599c9c8650108fa531eab018d528395eeccc7e2eb8a218f33","observation_id":"0d746626-898c-47a8-b593-a3cc3d4a830e","resolution":{"observed_at":"2026-08-11T15:18:19.765699Z","resolver_source":"raw_fallback","status":"parse_uncertain"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-11T15:18:19.745714Z","title":null,"venue":null,"work_id":"671013a9-c9a4-496a-a0c3-0f9cb66e2440","year":null},"citing_paper":{"arxiv_id":"2412.11170","last_updated":"2025-07-27T09:18:49Z","snapshot_observed_at":"2026-08-11T22:10:22.581543Z","submitted_at":"2024-12-15T12:41:44Z","title":"Benchmarking and Learning Multi-Dimensional Quality Evaluator for Text-to-3D Generation","version":2},"reference_index":94,"source":"pdf_text","source_observed_at":"2026-08-11T15:18:18.878836Z"},"links":{"citing_paper":"/paper/2412.11170"},"observation_digest":"sha256:907c3aa95aa550c60b6bb1e746f9737b79e18fea5383319fb588b83c391fe6a9","observation_id":"86c1be02-ef53-459b-906b-683ab8df63ad","resolution":{"observed_at":"2026-08-11T15:18:19.750304Z","resolver_source":"raw_fallback","status":"parse_uncertain"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-11T15:18:19.730473Z","title":null,"venue":null,"work_id":"bb7bd1cc-84fa-41b7-b4df-0c24d26d2254","year":null},"citing_paper":{"arxiv_id":"2412.11170","last_updated":"2025-07-27T09:18:49Z","snapshot_observed_at":"2026-08-11T22:10:22.581543Z","submitted_at":"2024-12-15T12:41:44Z","title":"Benchmarking and Learning Multi-Dimensional Quality Evaluator for Text-to-3D Generation","version":2},"reference_index":95,"source":"pdf_text","source_observed_at":"2026-08-11T15:18:18.883305Z"},"links":{"citing_paper":"/paper/2412.11170"},"observation_digest":"sha256:20e201545a0e3417b4dd93f8fefb5eaf6df2ce0eaf29c014b1504e6f93f69910","observation_id":"c9f455c0-ade9-4bec-8d77-2682e454b538","resolution":{"observed_at":"2026-08-11T15:18:19.735426Z","resolver_source":"raw_fallback","status":"parse_uncertain"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-11T15:18:19.700357Z","title":null,"venue":null,"work_id":"7b49ee05-5c4f-4443-a062-6044e37b79fc","year":null},"citing_paper":{"arxiv_id":"2412.11170","last_updated":"2025-07-27T09:18:49Z","snapshot_observed_at":"2026-08-11T22:10:22.581543Z","submitted_at":"2024-12-15T12:41:44Z","title":"Benchmarking and Learning Multi-Dimensional Quality Evaluator for Text-to-3D Generation","version":2},"reference_index":97,"source":"pdf_text","source_observed_at":"2026-08-11T15:18:18.893504Z"},"links":{"citing_paper":"/paper/2412.11170"},"observation_digest":"sha256:f57ea79463f95c767634388e199f1fd21c4ed5cca8e3c8d575b137678ec7b06e","observation_id":"4b465591-87ac-4836-9035-061012217fb0","resolution":{"observed_at":"2026-08-11T15:18:19.705438Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-11T15:18:19.685342Z","title":null,"venue":null,"work_id":"201626ce-e1e4-49af-a99c-5724ebfc2547","year":null},"citing_paper":{"arxiv_id":"2412.11170","last_updated":"2025-07-27T09:18:49Z","snapshot_observed_at":"2026-08-11T22:10:22.581543Z","submitted_at":"2024-12-15T12:41:44Z","title":"Benchmarking and Learning Multi-Dimensional Quality Evaluator for Text-to-3D Generation","version":2},"reference_index":98,"source":"pdf_text","source_observed_at":"2026-08-11T15:18:18.898906Z"},"links":{"citing_paper":"/paper/2412.11170"},"observation_digest":"sha256:ae6151e9aca70cf5cdce8720cf6077485c168d5fabd56e84f4a19629522f2af6","observation_id":"05347689-f25d-48b4-9eb1-19a62e2241f6","resolution":{"observed_at":"2026-08-11T15:18:19.690139Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-11T15:18:19.669519Z","title":null,"venue":null,"work_id":"28016b8e-41d8-41f9-a834-d3c7ad6abbd5","year":null},"citing_paper":{"arxiv_id":"2412.11170","last_updated":"2025-07-27T09:18:49Z","snapshot_observed_at":"2026-08-11T22:10:22.581543Z","submitted_at":"2024-12-15T12:41:44Z","title":"Benchmarking and Learning Multi-Dimensional Quality Evaluator for Text-to-3D Generation","version":2},"reference_index":99,"source":"pdf_text","source_observed_at":"2026-08-11T15:18:18.903719Z"},"links":{"citing_paper":"/paper/2412.11170"},"observation_digest":"sha256:76981895487f9fc145c73473ec9b208c64234a88883faa09614db31c595626d3","observation_id":"49b6a918-d5b9-4327-af43-ab74b3605819","resolution":{"observed_at":"2026-08-11T15:18:19.674244Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-11T15:18:19.715868Z","title":"Spatial","venue":null,"work_id":"f47a0e2b-9cac-4307-b4bd-a47c4e4552b2","year":null},"citing_paper":{"arxiv_id":"2412.11170","last_updated":"2025-07-27T09:18:49Z","snapshot_observed_at":"2026-08-11T22:10:22.581543Z","submitted_at":"2024-12-15T12:41:44Z","title":"Benchmarking and Learning Multi-Dimensional Quality Evaluator for Text-to-3D Generation","version":2},"reference_index":100,"source":"pdf_text","source_observed_at":"2026-08-11T15:18:18.887957Z"},"links":{"citing_paper":"/paper/2412.11170"},"observation_digest":"sha256:d665f0b11bb55dabd3a9a23c997e46b0a0dc7b6520e1efe5ba7449495c079198","observation_id":"ff934698-a206-46fe-bb75-4da0dc9fbe65","resolution":{"observed_at":"2026-08-11T15:18:19.720608Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-11T15:18:19.654170Z","title":null,"venue":null,"work_id":"98f238eb-cfe0-4a7d-9f4e-f861e33683d2","year":null},"citing_paper":{"arxiv_id":"2412.11170","last_updated":"2025-07-27T09:18:49Z","snapshot_observed_at":"2026-08-11T22:10:22.581543Z","submitted_at":"2024-12-15T12:41:44Z","title":"Benchmarking and Learning Multi-Dimensional Quality Evaluator for Text-to-3D Generation","version":2},"reference_index":101,"source":"pdf_text","source_observed_at":"2026-08-11T15:18:18.908204Z"},"links":{"citing_paper":"/paper/2412.11170"},"observation_digest":"sha256:db9a97d34a016086afd2252b3ff5a2919af4c72fd91605afcdfad7919343fdd3","observation_id":"4b152c5f-e607-41ea-bbc5-82d4c04c8e2f","resolution":{"observed_at":"2026-08-11T15:18:19.658923Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2412.11170","last_updated":"2025-07-27T09:18:49Z","latest_version":2,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-11T22:10:22.581543Z","submitted_at":"2024-12-15T12:41:44Z","title":"Benchmarking and Learning Multi-Dimensional Quality Evaluator for Text-to-3D Generation"},"reference_resolution":{"displayed":100,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":4,"unresolved":64,"verified_exact":0,"verified_fuzzy":32},"total_outbound_references":124},"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":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"thesis":"As of 12 August 2026, this Paper Citation Record lists 100 of 124 outbound references and 1 inbound Pith citation observation for arXiv:2412.11170."}