{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:IY5DRK5Q4YSKZUAEWRFGH4SGVB","short_pith_number":"pith:IY5DRK5Q","schema_version":"1.0","canonical_sha256":"463a38abb0e624acd004b44a63f246a86fff970835b7c01b35ddf666b5d76b2b","source":{"kind":"arxiv","id":"1907.11922","version":2},"attestation_state":"computed","paper":{"title":"MaskGAN: Towards Diverse and Interactive Facial Image Manipulation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.GR","cs.LG"],"primary_cat":"cs.CV","authors_text":"Cheng-Han Lee, Lingyun Wu, Ping Luo, Ziwei Liu","submitted_at":"2019-07-27T14:23:19Z","abstract_excerpt":"Facial image manipulation has achieved great progress in recent years. However, previous methods either operate on a predefined set of face attributes or leave users little freedom to interactively manipulate images. To overcome these drawbacks, we propose a novel framework termed MaskGAN, enabling diverse and interactive face manipulation. Our key insight is that semantic masks serve as a suitable intermediate representation for flexible face manipulation with fidelity preservation. MaskGAN has two main components: 1) Dense Mapping Network (DMN) and 2) Editing Behavior Simulated Training (EBS"},"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":"1907.11922","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-07-27T14:23:19Z","cross_cats_sorted":["cs.GR","cs.LG"],"title_canon_sha256":"be9a4e0653752a3c0fb3b08144560f94c929bc00be73917e305b3c2e5a8f9b15","abstract_canon_sha256":"9c0ffb3b905f5a15b0f591f4f9026d3e5322d9a2b84056dcf195d7727d89184b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:51:52.141848Z","signature_b64":"2iS0jFEloJ+XeyvlT5KU1sqUTFLys9k0/FzSQg5YbdJXIfp/evcLQdh4JdNlTHdv22pTVTNscDOyUBPvqGvEDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"463a38abb0e624acd004b44a63f246a86fff970835b7c01b35ddf666b5d76b2b","last_reissued_at":"2026-07-05T00:51:52.141417Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:51:52.141417Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"MaskGAN: Towards Diverse and Interactive Facial Image Manipulation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.GR","cs.LG"],"primary_cat":"cs.CV","authors_text":"Cheng-Han Lee, Lingyun Wu, Ping Luo, Ziwei Liu","submitted_at":"2019-07-27T14:23:19Z","abstract_excerpt":"Facial image manipulation has achieved great progress in recent years. However, previous methods either operate on a predefined set of face attributes or leave users little freedom to interactively manipulate images. To overcome these drawbacks, we propose a novel framework termed MaskGAN, enabling diverse and interactive face manipulation. Our key insight is that semantic masks serve as a suitable intermediate representation for flexible face manipulation with fidelity preservation. MaskGAN has two main components: 1) Dense Mapping Network (DMN) and 2) Editing Behavior Simulated Training (EBS"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1907.11922","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/1907.11922/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":"1907.11922","created_at":"2026-07-05T00:51:52.141473+00:00"},{"alias_kind":"arxiv_version","alias_value":"1907.11922v2","created_at":"2026-07-05T00:51:52.141473+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1907.11922","created_at":"2026-07-05T00:51:52.141473+00:00"},{"alias_kind":"pith_short_12","alias_value":"IY5DRK5Q4YSK","created_at":"2026-07-05T00:51:52.141473+00:00"},{"alias_kind":"pith_short_16","alias_value":"IY5DRK5Q4YSKZUAE","created_at":"2026-07-05T00:51:52.141473+00:00"},{"alias_kind":"pith_short_8","alias_value":"IY5DRK5Q","created_at":"2026-07-05T00:51:52.141473+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2506.02405","citing_title":"Modelship Attribution: Tracing Multi-Stage Manipulations Across Generative Models","ref_index":17,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/IY5DRK5Q4YSKZUAEWRFGH4SGVB","json":"https://pith.science/pith/IY5DRK5Q4YSKZUAEWRFGH4SGVB.json","graph_json":"https://pith.science/api/pith-number/IY5DRK5Q4YSKZUAEWRFGH4SGVB/graph.json","events_json":"https://pith.science/api/pith-number/IY5DRK5Q4YSKZUAEWRFGH4SGVB/events.json","paper":"https://pith.science/paper/IY5DRK5Q"},"agent_actions":{"view_html":"https://pith.science/pith/IY5DRK5Q4YSKZUAEWRFGH4SGVB","download_json":"https://pith.science/pith/IY5DRK5Q4YSKZUAEWRFGH4SGVB.json","view_paper":"https://pith.science/paper/IY5DRK5Q","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1907.11922&json=true","fetch_graph":"https://pith.science/api/pith-number/IY5DRK5Q4YSKZUAEWRFGH4SGVB/graph.json","fetch_events":"https://pith.science/api/pith-number/IY5DRK5Q4YSKZUAEWRFGH4SGVB/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/IY5DRK5Q4YSKZUAEWRFGH4SGVB/action/timestamp_anchor","attest_storage":"https://pith.science/pith/IY5DRK5Q4YSKZUAEWRFGH4SGVB/action/storage_attestation","attest_author":"https://pith.science/pith/IY5DRK5Q4YSKZUAEWRFGH4SGVB/action/author_attestation","sign_citation":"https://pith.science/pith/IY5DRK5Q4YSKZUAEWRFGH4SGVB/action/citation_signature","submit_replication":"https://pith.science/pith/IY5DRK5Q4YSKZUAEWRFGH4SGVB/action/replication_record"}},"created_at":"2026-07-05T00:51:52.141473+00:00","updated_at":"2026-07-05T00:51:52.141473+00:00"}