{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:GQBJ5ML2EFP63QLWPERZEH5H6T","short_pith_number":"pith:GQBJ5ML2","schema_version":"1.0","canonical_sha256":"34029eb17a215fedc1767923921fa7f4c075cd161026e1a5acd5f6b63adabed4","source":{"kind":"arxiv","id":"2412.14148","version":1},"attestation_state":"computed","paper":{"title":"MCMat: Multiview-Consistent and Physically Accurate PBR Material Generation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chao Xu, Hao Zhu, Lingteng Qiu, Shenhao Zhu, Siyu Zhu, Weihao Yuan, Xiaodong Gu, Xiaoguang Han, Xun Cao, Yao Yao, Yuxiao He, Zhe Li, Zhengyi Zhao, Zilong Dong","submitted_at":"2024-12-18T18:45:35Z","abstract_excerpt":"Existing 2D methods utilize UNet-based diffusion models to generate multi-view physically-based rendering (PBR) maps but struggle with multi-view inconsistency, while some 3D methods directly generate UV maps, encountering generalization issues due to the limited 3D data. To address these problems, we propose a two-stage approach, including multi-view generation and UV materials refinement. In the generation stage, we adopt a Diffusion Transformer (DiT) model to generate PBR materials, where both the specially designed multi-branch DiT and reference-based DiT blocks adopt a global attention me"},"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":"2412.14148","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-12-18T18:45:35Z","cross_cats_sorted":[],"title_canon_sha256":"1652ca38090207113c1431ef57a126f2c31b2a106e457f18e0215fa94fe69381","abstract_canon_sha256":"352e04277a42e52b366fc3fe1d443f77b80cfb6fd3955779be881c56a91a2cb4"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:51:14.370480Z","signature_b64":"DAWnxGd3A8Ludwh3yPzTypYwGoTsKD14bf8Mc9ApfkfRTR5+vJE8qI37cBjzVrHDeiqZk4tIIL2b/TJCpOcoDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"34029eb17a215fedc1767923921fa7f4c075cd161026e1a5acd5f6b63adabed4","last_reissued_at":"2026-07-05T09:51:14.369989Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:51:14.369989Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"MCMat: Multiview-Consistent and Physically Accurate PBR Material Generation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chao Xu, Hao Zhu, Lingteng Qiu, Shenhao Zhu, Siyu Zhu, Weihao Yuan, Xiaodong Gu, Xiaoguang Han, Xun Cao, Yao Yao, Yuxiao He, Zhe Li, Zhengyi Zhao, Zilong Dong","submitted_at":"2024-12-18T18:45:35Z","abstract_excerpt":"Existing 2D methods utilize UNet-based diffusion models to generate multi-view physically-based rendering (PBR) maps but struggle with multi-view inconsistency, while some 3D methods directly generate UV maps, encountering generalization issues due to the limited 3D data. To address these problems, we propose a two-stage approach, including multi-view generation and UV materials refinement. In the generation stage, we adopt a Diffusion Transformer (DiT) model to generate PBR materials, where both the specially designed multi-branch DiT and reference-based DiT blocks adopt a global attention me"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.14148","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/2412.14148/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":"2412.14148","created_at":"2026-07-05T09:51:14.370044+00:00"},{"alias_kind":"arxiv_version","alias_value":"2412.14148v1","created_at":"2026-07-05T09:51:14.370044+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.14148","created_at":"2026-07-05T09:51:14.370044+00:00"},{"alias_kind":"pith_short_12","alias_value":"GQBJ5ML2EFP6","created_at":"2026-07-05T09:51:14.370044+00:00"},{"alias_kind":"pith_short_16","alias_value":"GQBJ5ML2EFP63QLW","created_at":"2026-07-05T09:51:14.370044+00:00"},{"alias_kind":"pith_short_8","alias_value":"GQBJ5ML2","created_at":"2026-07-05T09:51:14.370044+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2505.22394","citing_title":"PacTure: Efficient PBR Texture Generation on Packed Views with Visual Autoregressive Models","ref_index":17,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/GQBJ5ML2EFP63QLWPERZEH5H6T","json":"https://pith.science/pith/GQBJ5ML2EFP63QLWPERZEH5H6T.json","graph_json":"https://pith.science/api/pith-number/GQBJ5ML2EFP63QLWPERZEH5H6T/graph.json","events_json":"https://pith.science/api/pith-number/GQBJ5ML2EFP63QLWPERZEH5H6T/events.json","paper":"https://pith.science/paper/GQBJ5ML2"},"agent_actions":{"view_html":"https://pith.science/pith/GQBJ5ML2EFP63QLWPERZEH5H6T","download_json":"https://pith.science/pith/GQBJ5ML2EFP63QLWPERZEH5H6T.json","view_paper":"https://pith.science/paper/GQBJ5ML2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2412.14148&json=true","fetch_graph":"https://pith.science/api/pith-number/GQBJ5ML2EFP63QLWPERZEH5H6T/graph.json","fetch_events":"https://pith.science/api/pith-number/GQBJ5ML2EFP63QLWPERZEH5H6T/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/GQBJ5ML2EFP63QLWPERZEH5H6T/action/timestamp_anchor","attest_storage":"https://pith.science/pith/GQBJ5ML2EFP63QLWPERZEH5H6T/action/storage_attestation","attest_author":"https://pith.science/pith/GQBJ5ML2EFP63QLWPERZEH5H6T/action/author_attestation","sign_citation":"https://pith.science/pith/GQBJ5ML2EFP63QLWPERZEH5H6T/action/citation_signature","submit_replication":"https://pith.science/pith/GQBJ5ML2EFP63QLWPERZEH5H6T/action/replication_record"}},"created_at":"2026-07-05T09:51:14.370044+00:00","updated_at":"2026-07-05T09:51:14.370044+00:00"}