{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:TVFSNYTB3FHV3ZWYPTMSO5VSAY","short_pith_number":"pith:TVFSNYTB","schema_version":"1.0","canonical_sha256":"9d4b26e261d94f5de6d87cd92776b20600376279ad17b9d26d3a422c7484eaec","source":{"kind":"arxiv","id":"1908.03852","version":1},"attestation_state":"computed","paper":{"title":"StructureFlow: Image Inpainting via Structure-aware Appearance Flow","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Ge Li, Ruonan Zhang, Shan Liu, Thomas H. Li, Xiaoming Yu, Yurui Ren","submitted_at":"2019-08-11T04:23:07Z","abstract_excerpt":"Image inpainting techniques have shown significant improvements by using deep neural networks recently. However, most of them may either fail to reconstruct reasonable structures or restore fine-grained textures. In order to solve this problem, in this paper, we propose a two-stage model which splits the inpainting task into two parts: structure reconstruction and texture generation. In the first stage, edge-preserved smooth images are employed to train a structure reconstructor which completes the missing structures of the inputs. In the second stage, based on the reconstructed structures, a "},"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":"1908.03852","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-08-11T04:23:07Z","cross_cats_sorted":[],"title_canon_sha256":"ef3b6fb6619544146febc93422bc6b2a23472bc2a867fdc5cf7c362bda70e2e9","abstract_canon_sha256":"e260011e4980b6b15965fac424a4f1afff6f354b10c2497a4db42f04b7619bd6"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-04T23:53:18.934819Z","signature_b64":"1PexkNPllVrkekUBQ5tYiVQkxGmB75c3uedBOJWr05ZLDuEBdoQAl844+DQ2lRLXoA9+Mhcps2n9CTLsaNG0Cg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9d4b26e261d94f5de6d87cd92776b20600376279ad17b9d26d3a422c7484eaec","last_reissued_at":"2026-07-04T23:53:18.934378Z","signature_status":"signed_v1","first_computed_at":"2026-07-04T23:53:18.934378Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"StructureFlow: Image Inpainting via Structure-aware Appearance Flow","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Ge Li, Ruonan Zhang, Shan Liu, Thomas H. Li, Xiaoming Yu, Yurui Ren","submitted_at":"2019-08-11T04:23:07Z","abstract_excerpt":"Image inpainting techniques have shown significant improvements by using deep neural networks recently. However, most of them may either fail to reconstruct reasonable structures or restore fine-grained textures. In order to solve this problem, in this paper, we propose a two-stage model which splits the inpainting task into two parts: structure reconstruction and texture generation. In the first stage, edge-preserved smooth images are employed to train a structure reconstructor which completes the missing structures of the inputs. In the second stage, based on the reconstructed structures, a "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1908.03852","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/1908.03852/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":"1908.03852","created_at":"2026-07-04T23:53:18.934439+00:00"},{"alias_kind":"arxiv_version","alias_value":"1908.03852v1","created_at":"2026-07-04T23:53:18.934439+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1908.03852","created_at":"2026-07-04T23:53:18.934439+00:00"},{"alias_kind":"pith_short_12","alias_value":"TVFSNYTB3FHV","created_at":"2026-07-04T23:53:18.934439+00:00"},{"alias_kind":"pith_short_16","alias_value":"TVFSNYTB3FHV3ZWY","created_at":"2026-07-04T23:53:18.934439+00:00"},{"alias_kind":"pith_short_8","alias_value":"TVFSNYTB","created_at":"2026-07-04T23:53:18.934439+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/TVFSNYTB3FHV3ZWYPTMSO5VSAY","json":"https://pith.science/pith/TVFSNYTB3FHV3ZWYPTMSO5VSAY.json","graph_json":"https://pith.science/api/pith-number/TVFSNYTB3FHV3ZWYPTMSO5VSAY/graph.json","events_json":"https://pith.science/api/pith-number/TVFSNYTB3FHV3ZWYPTMSO5VSAY/events.json","paper":"https://pith.science/paper/TVFSNYTB"},"agent_actions":{"view_html":"https://pith.science/pith/TVFSNYTB3FHV3ZWYPTMSO5VSAY","download_json":"https://pith.science/pith/TVFSNYTB3FHV3ZWYPTMSO5VSAY.json","view_paper":"https://pith.science/paper/TVFSNYTB","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1908.03852&json=true","fetch_graph":"https://pith.science/api/pith-number/TVFSNYTB3FHV3ZWYPTMSO5VSAY/graph.json","fetch_events":"https://pith.science/api/pith-number/TVFSNYTB3FHV3ZWYPTMSO5VSAY/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/TVFSNYTB3FHV3ZWYPTMSO5VSAY/action/timestamp_anchor","attest_storage":"https://pith.science/pith/TVFSNYTB3FHV3ZWYPTMSO5VSAY/action/storage_attestation","attest_author":"https://pith.science/pith/TVFSNYTB3FHV3ZWYPTMSO5VSAY/action/author_attestation","sign_citation":"https://pith.science/pith/TVFSNYTB3FHV3ZWYPTMSO5VSAY/action/citation_signature","submit_replication":"https://pith.science/pith/TVFSNYTB3FHV3ZWYPTMSO5VSAY/action/replication_record"}},"created_at":"2026-07-04T23:53:18.934439+00:00","updated_at":"2026-07-04T23:53:18.934439+00:00"}