{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:ZXJEOOG2N3NHAL3RW4XASHI2KS","short_pith_number":"pith:ZXJEOOG2","schema_version":"1.0","canonical_sha256":"cdd24738da6eda702f71b72e091d1a54a7c347aadcfbecc55bdaa57f62c91802","source":{"kind":"arxiv","id":"2312.15736","version":2},"attestation_state":"computed","paper":{"title":"Towards Real-World Blind Face Restoration with Generative Diffusion Prior","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jingfan Tan, Kaihao Zhang, Tao Wang, Wenhan Luo, Xiaochun Cao, Xiaoxu Chen","submitted_at":"2023-12-25T14:16:24Z","abstract_excerpt":"Blind face restoration is an important task in computer vision and has gained significant attention due to its wide-range applications. Previous works mainly exploit facial priors to restore face images and have demonstrated high-quality results. However, generating faithful facial details remains a challenging problem due to the limited prior knowledge obtained from finite data. In this work, we delve into the potential of leveraging the pretrained Stable Diffusion for blind face restoration. We propose BFRffusion which is thoughtfully designed to effectively extract features from low-quality"},"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":"2312.15736","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-12-25T14:16:24Z","cross_cats_sorted":[],"title_canon_sha256":"7e98ca4d2e40d812f47412439a7db3921399fbd70691df73b1bee12ed8c38d23","abstract_canon_sha256":"63971890cf284f45318e3da6794c50e68cb9a6fa601de658d02f9d8f10d4a209"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:57:29.525597Z","signature_b64":"96CFC0dnmASgKx7GJAuEG8cWC6jahFvp0fRHICaRFQLDyJtliy6rA9umj//NG0cimIwa5XPqa9EKLtAYwYUSDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"cdd24738da6eda702f71b72e091d1a54a7c347aadcfbecc55bdaa57f62c91802","last_reissued_at":"2026-07-05T07:57:29.525084Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:57:29.525084Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Towards Real-World Blind Face Restoration with Generative Diffusion Prior","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jingfan Tan, Kaihao Zhang, Tao Wang, Wenhan Luo, Xiaochun Cao, Xiaoxu Chen","submitted_at":"2023-12-25T14:16:24Z","abstract_excerpt":"Blind face restoration is an important task in computer vision and has gained significant attention due to its wide-range applications. Previous works mainly exploit facial priors to restore face images and have demonstrated high-quality results. However, generating faithful facial details remains a challenging problem due to the limited prior knowledge obtained from finite data. In this work, we delve into the potential of leveraging the pretrained Stable Diffusion for blind face restoration. We propose BFRffusion which is thoughtfully designed to effectively extract features from low-quality"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2312.15736","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/2312.15736/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":"2312.15736","created_at":"2026-07-05T07:57:29.525144+00:00"},{"alias_kind":"arxiv_version","alias_value":"2312.15736v2","created_at":"2026-07-05T07:57:29.525144+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2312.15736","created_at":"2026-07-05T07:57:29.525144+00:00"},{"alias_kind":"pith_short_12","alias_value":"ZXJEOOG2N3NH","created_at":"2026-07-05T07:57:29.525144+00:00"},{"alias_kind":"pith_short_16","alias_value":"ZXJEOOG2N3NHAL3R","created_at":"2026-07-05T07:57:29.525144+00:00"},{"alias_kind":"pith_short_8","alias_value":"ZXJEOOG2","created_at":"2026-07-05T07:57:29.525144+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2604.10532","citing_title":"The Second Challenge on Real-World Face Restoration at NTIRE 2026: Methods and Results","ref_index":8,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/ZXJEOOG2N3NHAL3RW4XASHI2KS","json":"https://pith.science/pith/ZXJEOOG2N3NHAL3RW4XASHI2KS.json","graph_json":"https://pith.science/api/pith-number/ZXJEOOG2N3NHAL3RW4XASHI2KS/graph.json","events_json":"https://pith.science/api/pith-number/ZXJEOOG2N3NHAL3RW4XASHI2KS/events.json","paper":"https://pith.science/paper/ZXJEOOG2"},"agent_actions":{"view_html":"https://pith.science/pith/ZXJEOOG2N3NHAL3RW4XASHI2KS","download_json":"https://pith.science/pith/ZXJEOOG2N3NHAL3RW4XASHI2KS.json","view_paper":"https://pith.science/paper/ZXJEOOG2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2312.15736&json=true","fetch_graph":"https://pith.science/api/pith-number/ZXJEOOG2N3NHAL3RW4XASHI2KS/graph.json","fetch_events":"https://pith.science/api/pith-number/ZXJEOOG2N3NHAL3RW4XASHI2KS/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ZXJEOOG2N3NHAL3RW4XASHI2KS/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ZXJEOOG2N3NHAL3RW4XASHI2KS/action/storage_attestation","attest_author":"https://pith.science/pith/ZXJEOOG2N3NHAL3RW4XASHI2KS/action/author_attestation","sign_citation":"https://pith.science/pith/ZXJEOOG2N3NHAL3RW4XASHI2KS/action/citation_signature","submit_replication":"https://pith.science/pith/ZXJEOOG2N3NHAL3RW4XASHI2KS/action/replication_record"}},"created_at":"2026-07-05T07:57:29.525144+00:00","updated_at":"2026-07-05T07:57:29.525144+00:00"}