{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:SNUKMIJFRAJI7OZ63PMPOLDAL3","short_pith_number":"pith:SNUKMIJF","canonical_record":{"source":{"id":"1905.04693","kind":"arxiv","version":5},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-05-12T11:11:24Z","cross_cats_sorted":[],"title_canon_sha256":"710e1d7ba0dae0f2ec0febdccf711771f3be33e3279e6cbd0cd9fa55118ebe07","abstract_canon_sha256":"93e2f069c1ba889953f909c0d4a9ebb82a58c47d4ab4cc583441f3476aba442a"},"schema_version":"1.0"},"canonical_sha256":"9368a6212588128fbb3edbd8f72c605ec487ee1a6235a1124eeb9cd1e4d81ddb","source":{"kind":"arxiv","id":"1905.04693","version":5},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1905.04693","created_at":"2026-07-05T06:02:43Z"},{"alias_kind":"arxiv_version","alias_value":"1905.04693v5","created_at":"2026-07-05T06:02:43Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1905.04693","created_at":"2026-07-05T06:02:43Z"},{"alias_kind":"pith_short_12","alias_value":"SNUKMIJFRAJI","created_at":"2026-07-05T06:02:43Z"},{"alias_kind":"pith_short_16","alias_value":"SNUKMIJFRAJI7OZ6","created_at":"2026-07-05T06:02:43Z"},{"alias_kind":"pith_short_8","alias_value":"SNUKMIJF","created_at":"2026-07-05T06:02:43Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:SNUKMIJFRAJI7OZ63PMPOLDAL3","target":"record","payload":{"canonical_record":{"source":{"id":"1905.04693","kind":"arxiv","version":5},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-05-12T11:11:24Z","cross_cats_sorted":[],"title_canon_sha256":"710e1d7ba0dae0f2ec0febdccf711771f3be33e3279e6cbd0cd9fa55118ebe07","abstract_canon_sha256":"93e2f069c1ba889953f909c0d4a9ebb82a58c47d4ab4cc583441f3476aba442a"},"schema_version":"1.0"},"canonical_sha256":"9368a6212588128fbb3edbd8f72c605ec487ee1a6235a1124eeb9cd1e4d81ddb","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:02:43.563634Z","signature_b64":"LCiqHMv3IULRrD/dnVyIcEs/uXzOwBFtyWpGlPKet/AKMe2SY7ftor6XDDGU771gc02afxoc4yH5i4a+KxfcBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9368a6212588128fbb3edbd8f72c605ec487ee1a6235a1124eeb9cd1e4d81ddb","last_reissued_at":"2026-07-05T06:02:43.563279Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:02:43.563279Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1905.04693","source_version":5,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T06:02:43Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"nJTtcDQp8TAV5h6lpx7FFgA7/r5mVT5BpyTcaMNHutwIWpLAUSNICDb0kfia94iORgGK1AO5hPUfb55oKex8DQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-22T19:42:43.487334Z"},"content_sha256":"b8ac63fd036502197223e03a05a71356d99ce312344f898615009ca96993a9a4","schema_version":"1.0","event_id":"sha256:b8ac63fd036502197223e03a05a71356d99ce312344f898615009ca96993a9a4"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:SNUKMIJFRAJI7OZ63PMPOLDAL3","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Hierarchy Composition GAN for High-fidelity Image Synthesis","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Fangneng Zhan, Jiaxing Huang, Shijian Lu","submitted_at":"2019-05-12T11:11:24Z","abstract_excerpt":"Despite the rapid progress of generative adversarial networks (GANs) in image synthesis in recent years, the existing image synthesis approaches work in either geometry domain or appearance domain alone which often introduces various synthesis artifacts. This paper presents an innovative Hierarchical Composition GAN (HIC-GAN) that incorporates image synthesis in geometry and appearance domains into an end-to-end trainable network and achieves superior synthesis realism in both domains simultaneously. We design an innovative hierarchical composition mechanism that is capable of learning realist"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1905.04693","kind":"arxiv","version":5},"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/1905.04693/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T06:02:43Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"cjvCGo9wYEW5MCQWXaLwwfVlHtXirAzMAKvTGoUnmoU5lFGjPQxacPvbN9Pl/mORPX5cMPso7a0bvjJdtSaUDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-22T19:42:43.487821Z"},"content_sha256":"cf797ec04ba551cead79030aa760c54937bedf5e98b6090b59415417322ea6f3","schema_version":"1.0","event_id":"sha256:cf797ec04ba551cead79030aa760c54937bedf5e98b6090b59415417322ea6f3"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/SNUKMIJFRAJI7OZ63PMPOLDAL3/bundle.json","state_url":"https://pith.science/pith/SNUKMIJFRAJI7OZ63PMPOLDAL3/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/SNUKMIJFRAJI7OZ63PMPOLDAL3/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-22T19:42:43Z","links":{"resolver":"https://pith.science/pith/SNUKMIJFRAJI7OZ63PMPOLDAL3","bundle":"https://pith.science/pith/SNUKMIJFRAJI7OZ63PMPOLDAL3/bundle.json","state":"https://pith.science/pith/SNUKMIJFRAJI7OZ63PMPOLDAL3/state.json","well_known_bundle":"https://pith.science/.well-known/pith/SNUKMIJFRAJI7OZ63PMPOLDAL3/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:SNUKMIJFRAJI7OZ63PMPOLDAL3","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"93e2f069c1ba889953f909c0d4a9ebb82a58c47d4ab4cc583441f3476aba442a","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-05-12T11:11:24Z","title_canon_sha256":"710e1d7ba0dae0f2ec0febdccf711771f3be33e3279e6cbd0cd9fa55118ebe07"},"schema_version":"1.0","source":{"id":"1905.04693","kind":"arxiv","version":5}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1905.04693","created_at":"2026-07-05T06:02:43Z"},{"alias_kind":"arxiv_version","alias_value":"1905.04693v5","created_at":"2026-07-05T06:02:43Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1905.04693","created_at":"2026-07-05T06:02:43Z"},{"alias_kind":"pith_short_12","alias_value":"SNUKMIJFRAJI","created_at":"2026-07-05T06:02:43Z"},{"alias_kind":"pith_short_16","alias_value":"SNUKMIJFRAJI7OZ6","created_at":"2026-07-05T06:02:43Z"},{"alias_kind":"pith_short_8","alias_value":"SNUKMIJF","created_at":"2026-07-05T06:02:43Z"}],"graph_snapshots":[{"event_id":"sha256:cf797ec04ba551cead79030aa760c54937bedf5e98b6090b59415417322ea6f3","target":"graph","created_at":"2026-07-05T06:02:43Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/1905.04693/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Despite the rapid progress of generative adversarial networks (GANs) in image synthesis in recent years, the existing image synthesis approaches work in either geometry domain or appearance domain alone which often introduces various synthesis artifacts. This paper presents an innovative Hierarchical Composition GAN (HIC-GAN) that incorporates image synthesis in geometry and appearance domains into an end-to-end trainable network and achieves superior synthesis realism in both domains simultaneously. We design an innovative hierarchical composition mechanism that is capable of learning realist","authors_text":"Fangneng Zhan, Jiaxing Huang, Shijian Lu","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-05-12T11:11:24Z","title":"Hierarchy Composition GAN for High-fidelity Image Synthesis"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1905.04693","kind":"arxiv","version":5},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:b8ac63fd036502197223e03a05a71356d99ce312344f898615009ca96993a9a4","target":"record","created_at":"2026-07-05T06:02:43Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"93e2f069c1ba889953f909c0d4a9ebb82a58c47d4ab4cc583441f3476aba442a","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-05-12T11:11:24Z","title_canon_sha256":"710e1d7ba0dae0f2ec0febdccf711771f3be33e3279e6cbd0cd9fa55118ebe07"},"schema_version":"1.0","source":{"id":"1905.04693","kind":"arxiv","version":5}},"canonical_sha256":"9368a6212588128fbb3edbd8f72c605ec487ee1a6235a1124eeb9cd1e4d81ddb","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"9368a6212588128fbb3edbd8f72c605ec487ee1a6235a1124eeb9cd1e4d81ddb","first_computed_at":"2026-07-05T06:02:43.563279Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T06:02:43.563279Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"LCiqHMv3IULRrD/dnVyIcEs/uXzOwBFtyWpGlPKet/AKMe2SY7ftor6XDDGU771gc02afxoc4yH5i4a+KxfcBQ==","signature_status":"signed_v1","signed_at":"2026-07-05T06:02:43.563634Z","signed_message":"canonical_sha256_bytes"},"source_id":"1905.04693","source_kind":"arxiv","source_version":5}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b8ac63fd036502197223e03a05a71356d99ce312344f898615009ca96993a9a4","sha256:cf797ec04ba551cead79030aa760c54937bedf5e98b6090b59415417322ea6f3"],"state_sha256":"31cb960ffdb3a5087e6b9a2f06eb2f231893e3a6d393b849f593d7b5b1f036f2"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"KPIWrDS4yspMNXtGVTLrZs4fMjbyzHdDIR9okPHyRr+mki+UQa9YKDT5DkZ8NyGynxI9OgcaNfvbzlEF9gs5AQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-22T19:42:43.491346Z","bundle_sha256":"e234ccaadeacbb046be3dccd442abdacd9018db2f56c48c1ef68b08631316c8a"}}