{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:JESYQPE2DEHNEUP55SAKIYW2PY","short_pith_number":"pith:JESYQPE2","schema_version":"1.0","canonical_sha256":"4925883c9a190ed251fdec80a462da7e27fd91af4e2293b547496cad40370943","source":{"kind":"arxiv","id":"2410.18974","version":2},"attestation_state":"computed","paper":{"title":"3D-Adapter: Geometry-Consistent Multi-View Diffusion for High-Quality 3D Generation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Bokui Shen, Connor Z. Lin, Gordon Wetzstein, Hansheng Chen, Hao Su, Jiayuan Gu, Leonidas Guibas, Linqi Zhou, Ruoxi Shi, Yulin Liu","submitted_at":"2024-10-24T17:59:30Z","abstract_excerpt":"Multi-view image diffusion models have significantly advanced open-domain 3D object generation. However, most existing models rely on 2D network architectures that lack inherent 3D biases, resulting in compromised geometric consistency. To address this challenge, we introduce 3D-Adapter, a plug-in module designed to infuse 3D geometry awareness into pretrained image diffusion models. Central to our approach is the idea of 3D feedback augmentation: for each denoising step in the sampling loop, 3D-Adapter decodes intermediate multi-view features into a coherent 3D representation, then re-encodes"},"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":"2410.18974","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-10-24T17:59:30Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"172eaca969343146e06380661c345676e4eba3713a863dfe507d470f867a1294","abstract_canon_sha256":"0ac1ce96ea91c3bfa06c1b5a840a96ad017361aa2979b8cb958f1e7539ec18c4"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:17:12.844694Z","signature_b64":"EnxDL5ntHqQW/yjCQ93StV26nwjrH1XvWWMEOFAbFxdPdx+LlimFWgHu93QiuHvnZcFDM+RQdblPZk56cbpNDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4925883c9a190ed251fdec80a462da7e27fd91af4e2293b547496cad40370943","last_reissued_at":"2026-07-05T10:17:12.844222Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:17:12.844222Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"3D-Adapter: Geometry-Consistent Multi-View Diffusion for High-Quality 3D Generation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Bokui Shen, Connor Z. Lin, Gordon Wetzstein, Hansheng Chen, Hao Su, Jiayuan Gu, Leonidas Guibas, Linqi Zhou, Ruoxi Shi, Yulin Liu","submitted_at":"2024-10-24T17:59:30Z","abstract_excerpt":"Multi-view image diffusion models have significantly advanced open-domain 3D object generation. However, most existing models rely on 2D network architectures that lack inherent 3D biases, resulting in compromised geometric consistency. To address this challenge, we introduce 3D-Adapter, a plug-in module designed to infuse 3D geometry awareness into pretrained image diffusion models. Central to our approach is the idea of 3D feedback augmentation: for each denoising step in the sampling loop, 3D-Adapter decodes intermediate multi-view features into a coherent 3D representation, then re-encodes"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.18974","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/2410.18974/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":"2410.18974","created_at":"2026-07-05T10:17:12.844278+00:00"},{"alias_kind":"arxiv_version","alias_value":"2410.18974v2","created_at":"2026-07-05T10:17:12.844278+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.18974","created_at":"2026-07-05T10:17:12.844278+00:00"},{"alias_kind":"pith_short_12","alias_value":"JESYQPE2DEHN","created_at":"2026-07-05T10:17:12.844278+00:00"},{"alias_kind":"pith_short_16","alias_value":"JESYQPE2DEHNEUP5","created_at":"2026-07-05T10:17:12.844278+00:00"},{"alias_kind":"pith_short_8","alias_value":"JESYQPE2","created_at":"2026-07-05T10:17:12.844278+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":7,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.24257","citing_title":"3DCarGen: Scalable 3D Car Generation via 3D-consistent Multi-view Synthesis","ref_index":15,"is_internal_anchor":false},{"citing_arxiv_id":"2606.24257","citing_title":"3DCarGen: Scalable 3D Car Generation via 3D-consistent Multi-view Synthesis","ref_index":15,"is_internal_anchor":false},{"citing_arxiv_id":"2605.10239","citing_title":"AdaptSplat: Adapting Vision Foundation Models for Feed-Forward 3D Gaussian Splatting","ref_index":3,"is_internal_anchor":false},{"citing_arxiv_id":"2605.18365","citing_title":"GeoFlow: Enforcing Implicit Geometric Consistency in Video Generation","ref_index":17,"is_internal_anchor":false},{"citing_arxiv_id":"2602.04349","citing_title":"VecSet-Edit: Unleashing Pre-trained LRM for Mesh Editing from Single Image","ref_index":2,"is_internal_anchor":false},{"citing_arxiv_id":"2605.10239","citing_title":"AdaptSplat: Adapting Vision Foundation Models for Feed-Forward 3D Gaussian Splatting","ref_index":3,"is_internal_anchor":false},{"citing_arxiv_id":"2605.01743","citing_title":"MOC-3D: Manifold-Order Consistency for Text-to-3D Generation","ref_index":4,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/JESYQPE2DEHNEUP55SAKIYW2PY","json":"https://pith.science/pith/JESYQPE2DEHNEUP55SAKIYW2PY.json","graph_json":"https://pith.science/api/pith-number/JESYQPE2DEHNEUP55SAKIYW2PY/graph.json","events_json":"https://pith.science/api/pith-number/JESYQPE2DEHNEUP55SAKIYW2PY/events.json","paper":"https://pith.science/paper/JESYQPE2"},"agent_actions":{"view_html":"https://pith.science/pith/JESYQPE2DEHNEUP55SAKIYW2PY","download_json":"https://pith.science/pith/JESYQPE2DEHNEUP55SAKIYW2PY.json","view_paper":"https://pith.science/paper/JESYQPE2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2410.18974&json=true","fetch_graph":"https://pith.science/api/pith-number/JESYQPE2DEHNEUP55SAKIYW2PY/graph.json","fetch_events":"https://pith.science/api/pith-number/JESYQPE2DEHNEUP55SAKIYW2PY/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/JESYQPE2DEHNEUP55SAKIYW2PY/action/timestamp_anchor","attest_storage":"https://pith.science/pith/JESYQPE2DEHNEUP55SAKIYW2PY/action/storage_attestation","attest_author":"https://pith.science/pith/JESYQPE2DEHNEUP55SAKIYW2PY/action/author_attestation","sign_citation":"https://pith.science/pith/JESYQPE2DEHNEUP55SAKIYW2PY/action/citation_signature","submit_replication":"https://pith.science/pith/JESYQPE2DEHNEUP55SAKIYW2PY/action/replication_record"}},"created_at":"2026-07-05T10:17:12.844278+00:00","updated_at":"2026-07-05T10:17:12.844278+00:00"}