{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:ORLYAVCGPW3L6YERQYAKB3QQN2","short_pith_number":"pith:ORLYAVCG","schema_version":"1.0","canonical_sha256":"74578054467db6bf60918600a0ee106e9c00215f2dd9cbe8ff672eac1a01c5a1","source":{"kind":"arxiv","id":"2503.08664","version":1},"attestation_state":"computed","paper":{"title":"MEAT: Multiview Diffusion Model for Human Generation on Megapixels with Mesh Attention","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Chen Change Loy, Fangzhou Hong, Liming Jiang, Shuai Yang, Wayne Wu, Yuhan Wang","submitted_at":"2025-03-11T17:50:59Z","abstract_excerpt":"Multiview diffusion models have shown considerable success in image-to-3D generation for general objects. However, when applied to human data, existing methods have yet to deliver promising results, largely due to the challenges of scaling multiview attention to higher resolutions. In this paper, we explore human multiview diffusion models at the megapixel level and introduce a solution called mesh attention to enable training at 1024x1024 resolution. Using a clothed human mesh as a central coarse geometric representation, the proposed mesh attention leverages rasterization and projection to e"},"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":"2503.08664","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-03-11T17:50:59Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"10d9f5492f7f656e176c6085c4bf56666561f74397cdf83a8d99e7162fb9e276","abstract_canon_sha256":"a896201a09871d72489af9d7c005cbd1d97c5d320c61fb75c47083cacae04ef1"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:29:04.674088Z","signature_b64":"MUgsZaZP81qUBgD9pVLjN0oVLgJf+j1R32L1rZN2PY07qOquIjj+RkptE7KPsYAfRC/O0EABI7OOrK4QrrqkCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"74578054467db6bf60918600a0ee106e9c00215f2dd9cbe8ff672eac1a01c5a1","last_reissued_at":"2026-07-05T10:29:04.673599Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:29:04.673599Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"MEAT: Multiview Diffusion Model for Human Generation on Megapixels with Mesh Attention","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Chen Change Loy, Fangzhou Hong, Liming Jiang, Shuai Yang, Wayne Wu, Yuhan Wang","submitted_at":"2025-03-11T17:50:59Z","abstract_excerpt":"Multiview diffusion models have shown considerable success in image-to-3D generation for general objects. However, when applied to human data, existing methods have yet to deliver promising results, largely due to the challenges of scaling multiview attention to higher resolutions. In this paper, we explore human multiview diffusion models at the megapixel level and introduce a solution called mesh attention to enable training at 1024x1024 resolution. Using a clothed human mesh as a central coarse geometric representation, the proposed mesh attention leverages rasterization and projection to e"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2503.08664","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/2503.08664/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":"2503.08664","created_at":"2026-07-05T10:29:04.673659+00:00"},{"alias_kind":"arxiv_version","alias_value":"2503.08664v1","created_at":"2026-07-05T10:29:04.673659+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2503.08664","created_at":"2026-07-05T10:29:04.673659+00:00"},{"alias_kind":"pith_short_12","alias_value":"ORLYAVCGPW3L","created_at":"2026-07-05T10:29:04.673659+00:00"},{"alias_kind":"pith_short_16","alias_value":"ORLYAVCGPW3L6YER","created_at":"2026-07-05T10:29:04.673659+00:00"},{"alias_kind":"pith_short_8","alias_value":"ORLYAVCG","created_at":"2026-07-05T10:29:04.673659+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/ORLYAVCGPW3L6YERQYAKB3QQN2","json":"https://pith.science/pith/ORLYAVCGPW3L6YERQYAKB3QQN2.json","graph_json":"https://pith.science/api/pith-number/ORLYAVCGPW3L6YERQYAKB3QQN2/graph.json","events_json":"https://pith.science/api/pith-number/ORLYAVCGPW3L6YERQYAKB3QQN2/events.json","paper":"https://pith.science/paper/ORLYAVCG"},"agent_actions":{"view_html":"https://pith.science/pith/ORLYAVCGPW3L6YERQYAKB3QQN2","download_json":"https://pith.science/pith/ORLYAVCGPW3L6YERQYAKB3QQN2.json","view_paper":"https://pith.science/paper/ORLYAVCG","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2503.08664&json=true","fetch_graph":"https://pith.science/api/pith-number/ORLYAVCGPW3L6YERQYAKB3QQN2/graph.json","fetch_events":"https://pith.science/api/pith-number/ORLYAVCGPW3L6YERQYAKB3QQN2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ORLYAVCGPW3L6YERQYAKB3QQN2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ORLYAVCGPW3L6YERQYAKB3QQN2/action/storage_attestation","attest_author":"https://pith.science/pith/ORLYAVCGPW3L6YERQYAKB3QQN2/action/author_attestation","sign_citation":"https://pith.science/pith/ORLYAVCGPW3L6YERQYAKB3QQN2/action/citation_signature","submit_replication":"https://pith.science/pith/ORLYAVCGPW3L6YERQYAKB3QQN2/action/replication_record"}},"created_at":"2026-07-05T10:29:04.673659+00:00","updated_at":"2026-07-05T10:29:04.673659+00:00"}