{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:T6OUHSV2UOXRVTE33MXE346M5A","short_pith_number":"pith:T6OUHSV2","schema_version":"1.0","canonical_sha256":"9f9d43cabaa3af1acc9bdb2e4df3cce81f280064fdff0cfab624138f10b805d6","source":{"kind":"arxiv","id":"2504.12451","version":1},"attestation_state":"computed","paper":{"title":"One Model to Rig Them All: Diverse Skeleton Rigging with UniRig","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.GR","authors_text":"Cheng-Feng Pu, Jia-Peng Zhang, Meng-Hao Guo, Shi-Min Hu, Yan-Pei Cao","submitted_at":"2025-04-16T19:32:11Z","abstract_excerpt":"The rapid evolution of 3D content creation, encompassing both AI-powered methods and traditional workflows, is driving an unprecedented demand for automated rigging solutions that can keep pace with the increasing complexity and diversity of 3D models. We introduce UniRig, a novel, unified framework for automatic skeletal rigging that leverages the power of large autoregressive models and a bone-point cross-attention mechanism to generate both high-quality skeletons and skinning weights. Unlike previous methods that struggle with complex or non-standard topologies, UniRig accurately predicts t"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2504.12451","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.GR","submitted_at":"2025-04-16T19:32:11Z","cross_cats_sorted":[],"title_canon_sha256":"74ed9a92a78898059aa486bcb35ab76e61cd65b35f22ebe8d1df472ad99aa42e","abstract_canon_sha256":"c3d56037ee1ca0bb3fe7682a3a0037c6a5e427ed6e5bc695e7f58495329667f5"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:50:16.593461Z","signature_b64":"0JU6fQJpNTsYVfLS9RuBj6/LjUCKWkBa3bpV/lJZFlN7L8eG7y4ej4oBHZf6Y/t0NmTZVN5lhrczBqibQVI1Cw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9f9d43cabaa3af1acc9bdb2e4df3cce81f280064fdff0cfab624138f10b805d6","last_reissued_at":"2026-07-05T10:50:16.592904Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:50:16.592904Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"One Model to Rig Them All: Diverse Skeleton Rigging with UniRig","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.GR","authors_text":"Cheng-Feng Pu, Jia-Peng Zhang, Meng-Hao Guo, Shi-Min Hu, Yan-Pei Cao","submitted_at":"2025-04-16T19:32:11Z","abstract_excerpt":"The rapid evolution of 3D content creation, encompassing both AI-powered methods and traditional workflows, is driving an unprecedented demand for automated rigging solutions that can keep pace with the increasing complexity and diversity of 3D models. We introduce UniRig, a novel, unified framework for automatic skeletal rigging that leverages the power of large autoregressive models and a bone-point cross-attention mechanism to generate both high-quality skeletons and skinning weights. Unlike previous methods that struggle with complex or non-standard topologies, UniRig accurately predicts t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.12451","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2504.12451/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2504.12451","created_at":"2026-07-05T10:50:16.592967+00:00"},{"alias_kind":"arxiv_version","alias_value":"2504.12451v1","created_at":"2026-07-05T10:50:16.592967+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2504.12451","created_at":"2026-07-05T10:50:16.592967+00:00"},{"alias_kind":"pith_short_12","alias_value":"T6OUHSV2UOXR","created_at":"2026-07-05T10:50:16.592967+00:00"},{"alias_kind":"pith_short_16","alias_value":"T6OUHSV2UOXRVTE3","created_at":"2026-07-05T10:50:16.592967+00:00"},{"alias_kind":"pith_short_8","alias_value":"T6OUHSV2","created_at":"2026-07-05T10:50:16.592967+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.25344","citing_title":"Follow Your Track: Precise Skeleton Animation Controlled by 3D Trajectories","ref_index":48,"is_internal_anchor":false},{"citing_arxiv_id":"2606.10988","citing_title":"AnimaSpark: A Feed-Forward Method for Animating Arbitrary 3D Objects","ref_index":28,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/T6OUHSV2UOXRVTE33MXE346M5A","json":"https://pith.science/pith/T6OUHSV2UOXRVTE33MXE346M5A.json","graph_json":"https://pith.science/api/pith-number/T6OUHSV2UOXRVTE33MXE346M5A/graph.json","events_json":"https://pith.science/api/pith-number/T6OUHSV2UOXRVTE33MXE346M5A/events.json","paper":"https://pith.science/paper/T6OUHSV2"},"agent_actions":{"view_html":"https://pith.science/pith/T6OUHSV2UOXRVTE33MXE346M5A","download_json":"https://pith.science/pith/T6OUHSV2UOXRVTE33MXE346M5A.json","view_paper":"https://pith.science/paper/T6OUHSV2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2504.12451&json=true","fetch_graph":"https://pith.science/api/pith-number/T6OUHSV2UOXRVTE33MXE346M5A/graph.json","fetch_events":"https://pith.science/api/pith-number/T6OUHSV2UOXRVTE33MXE346M5A/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/T6OUHSV2UOXRVTE33MXE346M5A/action/timestamp_anchor","attest_storage":"https://pith.science/pith/T6OUHSV2UOXRVTE33MXE346M5A/action/storage_attestation","attest_author":"https://pith.science/pith/T6OUHSV2UOXRVTE33MXE346M5A/action/author_attestation","sign_citation":"https://pith.science/pith/T6OUHSV2UOXRVTE33MXE346M5A/action/citation_signature","submit_replication":"https://pith.science/pith/T6OUHSV2UOXRVTE33MXE346M5A/action/replication_record"}},"created_at":"2026-07-05T10:50:16.592967+00:00","updated_at":"2026-07-05T10:50:16.592967+00:00"}