{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:H7KM5GOKMOA4GHDQA33WJUCCKD","short_pith_number":"pith:H7KM5GOK","schema_version":"1.0","canonical_sha256":"3fd4ce99ca6381c31c7006f764d04250c3ba4cab4a4a4d4182b31d8353a0e4f1","source":{"kind":"arxiv","id":"2507.20158","version":1},"attestation_state":"computed","paper":{"title":"AnimeColor: Reference-based Animation Colorization with Diffusion Transformers","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Danni Wu, Feng Wang, Han Wang, Li Song, Liyao Wang, Yuhong Zhang, Zuzeng Lin","submitted_at":"2025-07-27T07:25:08Z","abstract_excerpt":"Animation colorization plays a vital role in animation production, yet existing methods struggle to achieve color accuracy and temporal consistency. To address these challenges, we propose \\textbf{AnimeColor}, a novel reference-based animation colorization framework leveraging Diffusion Transformers (DiT). Our approach integrates sketch sequences into a DiT-based video diffusion model, enabling sketch-controlled animation generation. We introduce two key components: a High-level Color Extractor (HCE) to capture semantic color information and a Low-level Color Guider (LCG) to extract fine-grain"},"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":"2507.20158","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-07-27T07:25:08Z","cross_cats_sorted":[],"title_canon_sha256":"f79425e3d22326be305aaa0442fceb46686a8181a4e2a7f96a335df0e77be09c","abstract_canon_sha256":"ef4236b5348c1c3f66a253353e6aabe41ab3b81c9494589e994fb8b64197de5f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:44:14.365887Z","signature_b64":"6U3nbJQlWm6sZPC6bRTeQMDskOvltPKcwmjT5PfzdWgBlzlx9jMBkbQc+WdlilEGYj5h0DDhA7evMvt7rsgICQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3fd4ce99ca6381c31c7006f764d04250c3ba4cab4a4a4d4182b31d8353a0e4f1","last_reissued_at":"2026-07-05T11:44:14.365392Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:44:14.365392Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"AnimeColor: Reference-based Animation Colorization with Diffusion Transformers","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Danni Wu, Feng Wang, Han Wang, Li Song, Liyao Wang, Yuhong Zhang, Zuzeng Lin","submitted_at":"2025-07-27T07:25:08Z","abstract_excerpt":"Animation colorization plays a vital role in animation production, yet existing methods struggle to achieve color accuracy and temporal consistency. To address these challenges, we propose \\textbf{AnimeColor}, a novel reference-based animation colorization framework leveraging Diffusion Transformers (DiT). Our approach integrates sketch sequences into a DiT-based video diffusion model, enabling sketch-controlled animation generation. We introduce two key components: a High-level Color Extractor (HCE) to capture semantic color information and a Low-level Color Guider (LCG) to extract fine-grain"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.20158","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/2507.20158/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":"2507.20158","created_at":"2026-07-05T11:44:14.365482+00:00"},{"alias_kind":"arxiv_version","alias_value":"2507.20158v1","created_at":"2026-07-05T11:44:14.365482+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.20158","created_at":"2026-07-05T11:44:14.365482+00:00"},{"alias_kind":"pith_short_12","alias_value":"H7KM5GOKMOA4","created_at":"2026-07-05T11:44:14.365482+00:00"},{"alias_kind":"pith_short_16","alias_value":"H7KM5GOKMOA4GHDQ","created_at":"2026-07-05T11:44:14.365482+00:00"},{"alias_kind":"pith_short_8","alias_value":"H7KM5GOK","created_at":"2026-07-05T11:44:14.365482+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/H7KM5GOKMOA4GHDQA33WJUCCKD","json":"https://pith.science/pith/H7KM5GOKMOA4GHDQA33WJUCCKD.json","graph_json":"https://pith.science/api/pith-number/H7KM5GOKMOA4GHDQA33WJUCCKD/graph.json","events_json":"https://pith.science/api/pith-number/H7KM5GOKMOA4GHDQA33WJUCCKD/events.json","paper":"https://pith.science/paper/H7KM5GOK"},"agent_actions":{"view_html":"https://pith.science/pith/H7KM5GOKMOA4GHDQA33WJUCCKD","download_json":"https://pith.science/pith/H7KM5GOKMOA4GHDQA33WJUCCKD.json","view_paper":"https://pith.science/paper/H7KM5GOK","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2507.20158&json=true","fetch_graph":"https://pith.science/api/pith-number/H7KM5GOKMOA4GHDQA33WJUCCKD/graph.json","fetch_events":"https://pith.science/api/pith-number/H7KM5GOKMOA4GHDQA33WJUCCKD/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/H7KM5GOKMOA4GHDQA33WJUCCKD/action/timestamp_anchor","attest_storage":"https://pith.science/pith/H7KM5GOKMOA4GHDQA33WJUCCKD/action/storage_attestation","attest_author":"https://pith.science/pith/H7KM5GOKMOA4GHDQA33WJUCCKD/action/author_attestation","sign_citation":"https://pith.science/pith/H7KM5GOKMOA4GHDQA33WJUCCKD/action/citation_signature","submit_replication":"https://pith.science/pith/H7KM5GOKMOA4GHDQA33WJUCCKD/action/replication_record"}},"created_at":"2026-07-05T11:44:14.365482+00:00","updated_at":"2026-07-05T11:44:14.365482+00:00"}