{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:HGDNWTL2PH2VPJSBT24RLIRGX4","short_pith_number":"pith:HGDNWTL2","schema_version":"1.0","canonical_sha256":"3986db4d7a79f557a6419eb915a226bf1e857de3e0714fe6b6be4e16a28a7aed","source":{"kind":"arxiv","id":"1705.01088","version":2},"attestation_state":"computed","paper":{"title":"Visual Attribute Transfer through Deep Image Analogy","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Gang Hua, Jing Liao, Lu Yuan, Sing Bing Kang, Yuan Yao","submitted_at":"2017-05-02T17:44:01Z","abstract_excerpt":"We propose a new technique for visual attribute transfer across images that may have very different appearance but have perceptually similar semantic structure. By visual attribute transfer, we mean transfer of visual information (such as color, tone, texture, and style) from one image to another. For example, one image could be that of a painting or a sketch while the other is a photo of a real scene, and both depict the same type of scene.\n  Our technique finds semantically-meaningful dense correspondences between two input images. To accomplish this, it adapts the notion of \"image analogy\" "},"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":"1705.01088","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-05-02T17:44:01Z","cross_cats_sorted":[],"title_canon_sha256":"b7f9936c82e8b389928a5b23040afe9fc1ee7684296adda71e4fdf724d4c72a2","abstract_canon_sha256":"f63942b3e74bd4c39fc000bc3cd79872780149b9a9110ca3ebea5812599e83e3"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:42:57.467116Z","signature_b64":"wTyFaI+Iivw1Z+7U7NSU+RRR4keJmztxWNDEk/5wwTtwZuvbuhrWOIU8zj0qL1i6F5JQ/YOTuhGyd9Bj+kxaCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3986db4d7a79f557a6419eb915a226bf1e857de3e0714fe6b6be4e16a28a7aed","last_reissued_at":"2026-05-18T00:42:57.466387Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:42:57.466387Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Visual Attribute Transfer through Deep Image Analogy","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Gang Hua, Jing Liao, Lu Yuan, Sing Bing Kang, Yuan Yao","submitted_at":"2017-05-02T17:44:01Z","abstract_excerpt":"We propose a new technique for visual attribute transfer across images that may have very different appearance but have perceptually similar semantic structure. By visual attribute transfer, we mean transfer of visual information (such as color, tone, texture, and style) from one image to another. For example, one image could be that of a painting or a sketch while the other is a photo of a real scene, and both depict the same type of scene.\n  Our technique finds semantically-meaningful dense correspondences between two input images. To accomplish this, it adapts the notion of \"image analogy\" "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1705.01088","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":""},"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":"1705.01088","created_at":"2026-05-18T00:42:57.466499+00:00"},{"alias_kind":"arxiv_version","alias_value":"1705.01088v2","created_at":"2026-05-18T00:42:57.466499+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1705.01088","created_at":"2026-05-18T00:42:57.466499+00:00"},{"alias_kind":"pith_short_12","alias_value":"HGDNWTL2PH2V","created_at":"2026-05-18T12:31:18.294218+00:00"},{"alias_kind":"pith_short_16","alias_value":"HGDNWTL2PH2VPJSB","created_at":"2026-05-18T12:31:18.294218+00:00"},{"alias_kind":"pith_short_8","alias_value":"HGDNWTL2","created_at":"2026-05-18T12:31:18.294218+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":4,"internal_anchor_count":3,"sample":[{"citing_arxiv_id":"1906.09909","citing_title":"Deep Exemplar-based Video Colorization","ref_index":31,"is_internal_anchor":true},{"citing_arxiv_id":"1907.03118","citing_title":"Fast Universal Style Transfer for Artistic and Photorealistic Rendering","ref_index":22,"is_internal_anchor":true},{"citing_arxiv_id":"2205.11880","citing_title":"Hierarchical Vectorization for Portrait Images","ref_index":19,"is_internal_anchor":true},{"citing_arxiv_id":"2604.07795","citing_title":"Image-Guided Geometric Stylization of 3D Meshes","ref_index":32,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/HGDNWTL2PH2VPJSBT24RLIRGX4","json":"https://pith.science/pith/HGDNWTL2PH2VPJSBT24RLIRGX4.json","graph_json":"https://pith.science/api/pith-number/HGDNWTL2PH2VPJSBT24RLIRGX4/graph.json","events_json":"https://pith.science/api/pith-number/HGDNWTL2PH2VPJSBT24RLIRGX4/events.json","paper":"https://pith.science/paper/HGDNWTL2"},"agent_actions":{"view_html":"https://pith.science/pith/HGDNWTL2PH2VPJSBT24RLIRGX4","download_json":"https://pith.science/pith/HGDNWTL2PH2VPJSBT24RLIRGX4.json","view_paper":"https://pith.science/paper/HGDNWTL2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1705.01088&json=true","fetch_graph":"https://pith.science/api/pith-number/HGDNWTL2PH2VPJSBT24RLIRGX4/graph.json","fetch_events":"https://pith.science/api/pith-number/HGDNWTL2PH2VPJSBT24RLIRGX4/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/HGDNWTL2PH2VPJSBT24RLIRGX4/action/timestamp_anchor","attest_storage":"https://pith.science/pith/HGDNWTL2PH2VPJSBT24RLIRGX4/action/storage_attestation","attest_author":"https://pith.science/pith/HGDNWTL2PH2VPJSBT24RLIRGX4/action/author_attestation","sign_citation":"https://pith.science/pith/HGDNWTL2PH2VPJSBT24RLIRGX4/action/citation_signature","submit_replication":"https://pith.science/pith/HGDNWTL2PH2VPJSBT24RLIRGX4/action/replication_record"}},"created_at":"2026-05-18T00:42:57.466499+00:00","updated_at":"2026-05-18T00:42:57.466499+00:00"}