{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:NAS5UIKQ3DD4E7TFWHQ27F6J55","short_pith_number":"pith:NAS5UIKQ","canonical_record":{"source":{"id":"1801.00077","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-12-30T02:57:09Z","cross_cats_sorted":[],"title_canon_sha256":"9ccd25e0fef33fcd10632d7b7fe064c7b4f440001134897b2e552a0105504f59","abstract_canon_sha256":"892c94ce00ecbb0bbf64194b13c130d62522f0c397d851b2df642cf5f92832ab"},"schema_version":"1.0"},"canonical_sha256":"6825da2150d8c7c27e65b1e1af97c9ef785b6b9d5bdcab2cd7d4e57ede9c8252","source":{"kind":"arxiv","id":"1801.00077","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1801.00077","created_at":"2026-05-18T00:26:59Z"},{"alias_kind":"arxiv_version","alias_value":"1801.00077v1","created_at":"2026-05-18T00:26:59Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1801.00077","created_at":"2026-05-18T00:26:59Z"},{"alias_kind":"pith_short_12","alias_value":"NAS5UIKQ3DD4","created_at":"2026-05-18T12:31:31Z"},{"alias_kind":"pith_short_16","alias_value":"NAS5UIKQ3DD4E7TF","created_at":"2026-05-18T12:31:31Z"},{"alias_kind":"pith_short_8","alias_value":"NAS5UIKQ","created_at":"2026-05-18T12:31:31Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:NAS5UIKQ3DD4E7TFWHQ27F6J55","target":"record","payload":{"canonical_record":{"source":{"id":"1801.00077","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-12-30T02:57:09Z","cross_cats_sorted":[],"title_canon_sha256":"9ccd25e0fef33fcd10632d7b7fe064c7b4f440001134897b2e552a0105504f59","abstract_canon_sha256":"892c94ce00ecbb0bbf64194b13c130d62522f0c397d851b2df642cf5f92832ab"},"schema_version":"1.0"},"canonical_sha256":"6825da2150d8c7c27e65b1e1af97c9ef785b6b9d5bdcab2cd7d4e57ede9c8252","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:26:59.529348Z","signature_b64":"3ha+LEue6dE74gvxP/2l7lzdrtgjxJjoi8s/XOCNGY6xsB/0yaS/fFMBdOwM9W7VVLPCnMTxUASgdrduRE7EAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6825da2150d8c7c27e65b1e1af97c9ef785b6b9d5bdcab2cd7d4e57ede9c8252","last_reissued_at":"2026-05-18T00:26:59.528664Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:26:59.528664Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1801.00077","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:26:59Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"rH6VlEhzhCYee5iu6yoB0XpDhX6tIbk3EROOKNonBuQLy0/1dsnO3eqP6SsDixf3C8B8TQ7KCOgpFIjY35TnCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-21T11:45:58.056733Z"},"content_sha256":"98bf77ec336859f77c42a4378dff822688fdb5f33ee79f2a120ed86a83191e5d","schema_version":"1.0","event_id":"sha256:98bf77ec336859f77c42a4378dff822688fdb5f33ee79f2a120ed86a83191e5d"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:NAS5UIKQ3DD4E7TFWHQ27F6J55","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Face Synthesis from Visual Attributes via Sketch using Conditional VAEs and GANs","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Vishal M. Patel, Xing Di","submitted_at":"2017-12-30T02:57:09Z","abstract_excerpt":"Automatic synthesis of faces from visual attributes is an important problem in computer vision and has wide applications in law enforcement and entertainment. With the advent of deep generative convolutional neural networks (CNNs), attempts have been made to synthesize face images from attributes and text descriptions. In this paper, we take a different approach, where we formulate the original problem as a stage-wise learning problem. We first synthesize the facial sketch corresponding to the visual attributes and then we reconstruct the face image based on the synthesized sketch. The propose"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1801.00077","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":""},"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":"2026-05-18T00:26:59Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"N31p+NNsSS0s5BRHd65yDBvcNNjdl68T+754oUy8E+f+LKUx5I5+rM8Gi+EDSIuysAM90SXSpEeniuCK8a5IDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-21T11:45:58.057078Z"},"content_sha256":"87265700884d2d5bed1594a0aa48f646c3cf92e5c69a189bb9e975bec07d19b6","schema_version":"1.0","event_id":"sha256:87265700884d2d5bed1594a0aa48f646c3cf92e5c69a189bb9e975bec07d19b6"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/NAS5UIKQ3DD4E7TFWHQ27F6J55/bundle.json","state_url":"https://pith.science/pith/NAS5UIKQ3DD4E7TFWHQ27F6J55/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/NAS5UIKQ3DD4E7TFWHQ27F6J55/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":"2026-05-21T11:45:58Z","links":{"resolver":"https://pith.science/pith/NAS5UIKQ3DD4E7TFWHQ27F6J55","bundle":"https://pith.science/pith/NAS5UIKQ3DD4E7TFWHQ27F6J55/bundle.json","state":"https://pith.science/pith/NAS5UIKQ3DD4E7TFWHQ27F6J55/state.json","well_known_bundle":"https://pith.science/.well-known/pith/NAS5UIKQ3DD4E7TFWHQ27F6J55/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:NAS5UIKQ3DD4E7TFWHQ27F6J55","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":"892c94ce00ecbb0bbf64194b13c130d62522f0c397d851b2df642cf5f92832ab","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-12-30T02:57:09Z","title_canon_sha256":"9ccd25e0fef33fcd10632d7b7fe064c7b4f440001134897b2e552a0105504f59"},"schema_version":"1.0","source":{"id":"1801.00077","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1801.00077","created_at":"2026-05-18T00:26:59Z"},{"alias_kind":"arxiv_version","alias_value":"1801.00077v1","created_at":"2026-05-18T00:26:59Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1801.00077","created_at":"2026-05-18T00:26:59Z"},{"alias_kind":"pith_short_12","alias_value":"NAS5UIKQ3DD4","created_at":"2026-05-18T12:31:31Z"},{"alias_kind":"pith_short_16","alias_value":"NAS5UIKQ3DD4E7TF","created_at":"2026-05-18T12:31:31Z"},{"alias_kind":"pith_short_8","alias_value":"NAS5UIKQ","created_at":"2026-05-18T12:31:31Z"}],"graph_snapshots":[{"event_id":"sha256:87265700884d2d5bed1594a0aa48f646c3cf92e5c69a189bb9e975bec07d19b6","target":"graph","created_at":"2026-05-18T00:26:59Z","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"},"paper":{"abstract_excerpt":"Automatic synthesis of faces from visual attributes is an important problem in computer vision and has wide applications in law enforcement and entertainment. With the advent of deep generative convolutional neural networks (CNNs), attempts have been made to synthesize face images from attributes and text descriptions. In this paper, we take a different approach, where we formulate the original problem as a stage-wise learning problem. We first synthesize the facial sketch corresponding to the visual attributes and then we reconstruct the face image based on the synthesized sketch. The propose","authors_text":"Vishal M. Patel, Xing Di","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-12-30T02:57:09Z","title":"Face Synthesis from Visual Attributes via Sketch using Conditional VAEs and GANs"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1801.00077","kind":"arxiv","version":1},"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":"sha256:98bf77ec336859f77c42a4378dff822688fdb5f33ee79f2a120ed86a83191e5d","target":"record","created_at":"2026-05-18T00:26:59Z","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":"892c94ce00ecbb0bbf64194b13c130d62522f0c397d851b2df642cf5f92832ab","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-12-30T02:57:09Z","title_canon_sha256":"9ccd25e0fef33fcd10632d7b7fe064c7b4f440001134897b2e552a0105504f59"},"schema_version":"1.0","source":{"id":"1801.00077","kind":"arxiv","version":1}},"canonical_sha256":"6825da2150d8c7c27e65b1e1af97c9ef785b6b9d5bdcab2cd7d4e57ede9c8252","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"6825da2150d8c7c27e65b1e1af97c9ef785b6b9d5bdcab2cd7d4e57ede9c8252","first_computed_at":"2026-05-18T00:26:59.528664Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:26:59.528664Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"3ha+LEue6dE74gvxP/2l7lzdrtgjxJjoi8s/XOCNGY6xsB/0yaS/fFMBdOwM9W7VVLPCnMTxUASgdrduRE7EAg==","signature_status":"signed_v1","signed_at":"2026-05-18T00:26:59.529348Z","signed_message":"canonical_sha256_bytes"},"source_id":"1801.00077","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:98bf77ec336859f77c42a4378dff822688fdb5f33ee79f2a120ed86a83191e5d","sha256:87265700884d2d5bed1594a0aa48f646c3cf92e5c69a189bb9e975bec07d19b6"],"state_sha256":"e6a5f7e605afa49b9f5724a9d9987848ef011b16f682e017054e2be9eebddf6b"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ONNc26MRYbbyZ31FXCPXVXEx73l08dooGRYjfjitvePFG8G09MLST3h8KeJ1uJ5/xAtcbVfwwVR5kmInIarDAg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-21T11:45:58.059255Z","bundle_sha256":"e535ba49a294b6957ee3e7e378b4c90f498cb79cc001b10d46facca9ab01f93d"}}