{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:ZUHP3ZYZHNHUD4YTHVFODAVCRG","short_pith_number":"pith:ZUHP3ZYZ","schema_version":"1.0","canonical_sha256":"cd0efde7193b4f41f3133d4ae182a2899c22cfea34431920e5096f4d1e02fb6b","source":{"kind":"arxiv","id":"2412.11596","version":2},"attestation_state":"computed","paper":{"title":"MeshArt: Generating Articulated Meshes with Structure-Guided Transformers","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.GR"],"primary_cat":"cs.CV","authors_text":"Angela Dai, Daoyi Gao, Lei Li, Yawar Siddiqui","submitted_at":"2024-12-16T09:35:08Z","abstract_excerpt":"Articulated 3D object generation is fundamental for creating realistic, functional, and interactable virtual assets which are not simply static. We introduce MeshArt, a hierarchical transformer-based approach to generate articulated 3D meshes with clean, compact geometry, reminiscent of human-crafted 3D models. We approach articulated mesh generation in a part-by-part fashion across two stages. First, we generate a high-level articulation-aware object structure; then, based on this structural information, we synthesize each part's mesh faces. Key to our approach is modeling both articulation s"},"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":"2412.11596","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-12-16T09:35:08Z","cross_cats_sorted":["cs.GR"],"title_canon_sha256":"29e3ffc4747bef00d99c47052af68385c2a1572c5e97652eeb283764b69cedc3","abstract_canon_sha256":"9bf55cf401246a9cc320841127a1f6325dc50318e23f53f0e47d28f580b64cd0"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:17:39.158862Z","signature_b64":"5pNG+BsX15cmK0RlgGUqjJbn0nt2GpWY8qtXokcDi6WZhijCvFIUPoYI3I5voP/HY9eIpmH5cGX/RCVSO7csDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"cd0efde7193b4f41f3133d4ae182a2899c22cfea34431920e5096f4d1e02fb6b","last_reissued_at":"2026-07-05T11:17:39.158372Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:17:39.158372Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"MeshArt: Generating Articulated Meshes with Structure-Guided Transformers","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.GR"],"primary_cat":"cs.CV","authors_text":"Angela Dai, Daoyi Gao, Lei Li, Yawar Siddiqui","submitted_at":"2024-12-16T09:35:08Z","abstract_excerpt":"Articulated 3D object generation is fundamental for creating realistic, functional, and interactable virtual assets which are not simply static. We introduce MeshArt, a hierarchical transformer-based approach to generate articulated 3D meshes with clean, compact geometry, reminiscent of human-crafted 3D models. We approach articulated mesh generation in a part-by-part fashion across two stages. First, we generate a high-level articulation-aware object structure; then, based on this structural information, we synthesize each part's mesh faces. Key to our approach is modeling both articulation s"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.11596","kind":"arxiv","version":2},"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/2412.11596/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":"2412.11596","created_at":"2026-07-05T11:17:39.158432+00:00"},{"alias_kind":"arxiv_version","alias_value":"2412.11596v2","created_at":"2026-07-05T11:17:39.158432+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.11596","created_at":"2026-07-05T11:17:39.158432+00:00"},{"alias_kind":"pith_short_12","alias_value":"ZUHP3ZYZHNHU","created_at":"2026-07-05T11:17:39.158432+00:00"},{"alias_kind":"pith_short_16","alias_value":"ZUHP3ZYZHNHUD4YT","created_at":"2026-07-05T11:17:39.158432+00:00"},{"alias_kind":"pith_short_8","alias_value":"ZUHP3ZYZ","created_at":"2026-07-05T11:17:39.158432+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.23489","citing_title":"MeshFlow: Mesh Generation with Equivariant Flow Matching","ref_index":107,"is_internal_anchor":false},{"citing_arxiv_id":"2601.22858","citing_title":"Learning to Build Shapes by Extrusion","ref_index":10,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/ZUHP3ZYZHNHUD4YTHVFODAVCRG","json":"https://pith.science/pith/ZUHP3ZYZHNHUD4YTHVFODAVCRG.json","graph_json":"https://pith.science/api/pith-number/ZUHP3ZYZHNHUD4YTHVFODAVCRG/graph.json","events_json":"https://pith.science/api/pith-number/ZUHP3ZYZHNHUD4YTHVFODAVCRG/events.json","paper":"https://pith.science/paper/ZUHP3ZYZ"},"agent_actions":{"view_html":"https://pith.science/pith/ZUHP3ZYZHNHUD4YTHVFODAVCRG","download_json":"https://pith.science/pith/ZUHP3ZYZHNHUD4YTHVFODAVCRG.json","view_paper":"https://pith.science/paper/ZUHP3ZYZ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2412.11596&json=true","fetch_graph":"https://pith.science/api/pith-number/ZUHP3ZYZHNHUD4YTHVFODAVCRG/graph.json","fetch_events":"https://pith.science/api/pith-number/ZUHP3ZYZHNHUD4YTHVFODAVCRG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ZUHP3ZYZHNHUD4YTHVFODAVCRG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ZUHP3ZYZHNHUD4YTHVFODAVCRG/action/storage_attestation","attest_author":"https://pith.science/pith/ZUHP3ZYZHNHUD4YTHVFODAVCRG/action/author_attestation","sign_citation":"https://pith.science/pith/ZUHP3ZYZHNHUD4YTHVFODAVCRG/action/citation_signature","submit_replication":"https://pith.science/pith/ZUHP3ZYZHNHUD4YTHVFODAVCRG/action/replication_record"}},"created_at":"2026-07-05T11:17:39.158432+00:00","updated_at":"2026-07-05T11:17:39.158432+00:00"}