{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:URC7YRFADBQH35HE72JWSTYO4F","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"7cb9aae467ebb6ec005c60652b70c395ee6043e1e139762568081053c62aa07f","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-09-05T14:18:52Z","title_canon_sha256":"355850cc84fc5fa50afa5ecf4b729f8c892e5c71088d895f8e43b86344468bfd"},"schema_version":"1.0","source":{"id":"2509.05131","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2509.05131","created_at":"2026-07-05T12:05:36Z"},{"alias_kind":"arxiv_version","alias_value":"2509.05131v1","created_at":"2026-07-05T12:05:36Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2509.05131","created_at":"2026-07-05T12:05:36Z"},{"alias_kind":"pith_short_12","alias_value":"URC7YRFADBQH","created_at":"2026-07-05T12:05:36Z"},{"alias_kind":"pith_short_16","alias_value":"URC7YRFADBQH35HE","created_at":"2026-07-05T12:05:36Z"},{"alias_kind":"pith_short_8","alias_value":"URC7YRFA","created_at":"2026-07-05T12:05:36Z"}],"graph_snapshots":[{"event_id":"sha256:4b7ec3be41f6ff3e9edb44215ec5854698b29ab86134e60e25ab87d8d0a67d77","target":"graph","created_at":"2026-07-05T12:05:36Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2509.05131/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"High-quality textures are critical for realistic 3D content creation, yet existing generative methods are slow, rely on UV maps, and often fail to remain faithful to a reference image. To address these challenges, we propose a transformer-based framework that predicts a 3D texture field directly from a single image and a mesh, eliminating the need for UV mapping and differentiable rendering, and enabling faster texture generation. Our method integrates a triplane representation with depth-based backprojection losses, enabling efficient training and faster inference. Once trained, it generates ","authors_text":"AmirHossein Zamani, Arianna Rampini, Bruno Roy, Derek Cheung, Kanika Madan","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-09-05T14:18:52Z","title":"A Scalable Attention-Based Approach for Image-to-3D Texture Mapping"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2509.05131","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:904cdf5e0f8b185778871340bbdc926f84ccab9f48a91d328a3bb226a0d1bedd","target":"record","created_at":"2026-07-05T12:05:36Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"7cb9aae467ebb6ec005c60652b70c395ee6043e1e139762568081053c62aa07f","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-09-05T14:18:52Z","title_canon_sha256":"355850cc84fc5fa50afa5ecf4b729f8c892e5c71088d895f8e43b86344468bfd"},"schema_version":"1.0","source":{"id":"2509.05131","kind":"arxiv","version":1}},"canonical_sha256":"a445fc44a018607df4e4fe93694f0ee14865aca8029a25ba753140af1a54e3bf","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"a445fc44a018607df4e4fe93694f0ee14865aca8029a25ba753140af1a54e3bf","first_computed_at":"2026-07-05T12:05:36.612869Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T12:05:36.612869Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"fseB7idC/OAoEdwEptRqXMPKYCXwtnEhPCsNUCLy0yU8R1A/zaKi75qOAIDRB6J5yLLcUfuSBPxgEa0zABEUAQ==","signature_status":"signed_v1","signed_at":"2026-07-05T12:05:36.613490Z","signed_message":"canonical_sha256_bytes"},"source_id":"2509.05131","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:904cdf5e0f8b185778871340bbdc926f84ccab9f48a91d328a3bb226a0d1bedd","sha256:4b7ec3be41f6ff3e9edb44215ec5854698b29ab86134e60e25ab87d8d0a67d77"],"state_sha256":"cc036e7abc1f9619682503fb5d6fc1359c8baface4ea33a25f346c1ed729582a"}