{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:I3LIQ32AENEMLIURH7N4XJNESI","short_pith_number":"pith:I3LIQ32A","canonical_record":{"source":{"id":"1902.05978","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2019-02-15T19:53:45Z","cross_cats_sorted":[],"title_canon_sha256":"b56a600556c1beaf26ec83416ab424c5cdc6164b35c0e3617b7a81898992f179","abstract_canon_sha256":"f0aace9f743ae3153b60744a2d46e817a3bfdd8287eb7207fc63cb520a5a2144"},"schema_version":"1.0"},"canonical_sha256":"46d6886f402348c5a2913fdbcba5a492001e4a3d73f28225bce4630f70fc30ab","source":{"kind":"arxiv","id":"1902.05978","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1902.05978","created_at":"2026-07-05T01:33:33Z"},{"alias_kind":"arxiv_version","alias_value":"1902.05978v2","created_at":"2026-07-05T01:33:33Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1902.05978","created_at":"2026-07-05T01:33:33Z"},{"alias_kind":"pith_short_12","alias_value":"I3LIQ32AENEM","created_at":"2026-07-05T01:33:33Z"},{"alias_kind":"pith_short_16","alias_value":"I3LIQ32AENEMLIUR","created_at":"2026-07-05T01:33:33Z"},{"alias_kind":"pith_short_8","alias_value":"I3LIQ32A","created_at":"2026-07-05T01:33:33Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:I3LIQ32AENEMLIURH7N4XJNESI","target":"record","payload":{"canonical_record":{"source":{"id":"1902.05978","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2019-02-15T19:53:45Z","cross_cats_sorted":[],"title_canon_sha256":"b56a600556c1beaf26ec83416ab424c5cdc6164b35c0e3617b7a81898992f179","abstract_canon_sha256":"f0aace9f743ae3153b60744a2d46e817a3bfdd8287eb7207fc63cb520a5a2144"},"schema_version":"1.0"},"canonical_sha256":"46d6886f402348c5a2913fdbcba5a492001e4a3d73f28225bce4630f70fc30ab","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:33:33.094709Z","signature_b64":"EKejFp8EQQFLmNpTfkmjj9h0J95AePJz3YpqVas/WBbh+MoBA7bd+eejxJD1JFE6oBOawJROvtLenYa+kDDoCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"46d6886f402348c5a2913fdbcba5a492001e4a3d73f28225bce4630f70fc30ab","last_reissued_at":"2026-07-05T01:33:33.094214Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:33:33.094214Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1902.05978","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T01:33:33Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"vdJQujDi+1M8ZVPesVPqauRPLHZo4QfG64Nk/CBOC7yv1SmzoH5GVc/mdFg+Sk5cgic9pPoc/SqFBvU11m1SDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-03T20:22:16.303932Z"},"content_sha256":"787fb4f81c8b6d5448b1e9b4143beedc057bcbb8a37960f913afb9eb47193d8f","schema_version":"1.0","event_id":"sha256:787fb4f81c8b6d5448b1e9b4143beedc057bcbb8a37960f913afb9eb47193d8f"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:I3LIQ32AENEMLIURH7N4XJNESI","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"GANFIT: Generative Adversarial Network Fitting for High Fidelity 3D Face Reconstruction","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Baris Gecer, Irene Kotsia, Stefanos Zafeiriou, Stylianos Ploumpis","submitted_at":"2019-02-15T19:53:45Z","abstract_excerpt":"In the past few years, a lot of work has been done towards reconstructing the 3D facial structure from single images by capitalizing on the power of Deep Convolutional Neural Networks (DCNNs). In the most recent works, differentiable renderers were employed in order to learn the relationship between the facial identity features and the parameters of a 3D morphable model for shape and texture. The texture features either correspond to components of a linear texture space or are learned by auto-encoders directly from in-the-wild images. In all cases, the quality of the facial texture reconstruct"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1902.05978","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/1902.05978/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":"2026-07-05T01:33:33Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"l29jmHwcH3AcwK79f5IgbHoxhSofXyJ8thjBdXrzH++tWH+ebmLmMntAyd0IZh45Bygwr6XEKaoFZY3/yPyVBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-03T20:22:16.304403Z"},"content_sha256":"f5d9a5ae113745627bdaee77e11f0b7a865221171710a671c54a083b091677b0","schema_version":"1.0","event_id":"sha256:f5d9a5ae113745627bdaee77e11f0b7a865221171710a671c54a083b091677b0"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/I3LIQ32AENEMLIURH7N4XJNESI/bundle.json","state_url":"https://pith.science/pith/I3LIQ32AENEMLIURH7N4XJNESI/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/I3LIQ32AENEMLIURH7N4XJNESI/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":"2026-08-03T20:22:16Z","links":{"resolver":"https://pith.science/pith/I3LIQ32AENEMLIURH7N4XJNESI","bundle":"https://pith.science/pith/I3LIQ32AENEMLIURH7N4XJNESI/bundle.json","state":"https://pith.science/pith/I3LIQ32AENEMLIURH7N4XJNESI/state.json","well_known_bundle":"https://pith.science/.well-known/pith/I3LIQ32AENEMLIURH7N4XJNESI/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:I3LIQ32AENEMLIURH7N4XJNESI","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":"f0aace9f743ae3153b60744a2d46e817a3bfdd8287eb7207fc63cb520a5a2144","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2019-02-15T19:53:45Z","title_canon_sha256":"b56a600556c1beaf26ec83416ab424c5cdc6164b35c0e3617b7a81898992f179"},"schema_version":"1.0","source":{"id":"1902.05978","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1902.05978","created_at":"2026-07-05T01:33:33Z"},{"alias_kind":"arxiv_version","alias_value":"1902.05978v2","created_at":"2026-07-05T01:33:33Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1902.05978","created_at":"2026-07-05T01:33:33Z"},{"alias_kind":"pith_short_12","alias_value":"I3LIQ32AENEM","created_at":"2026-07-05T01:33:33Z"},{"alias_kind":"pith_short_16","alias_value":"I3LIQ32AENEMLIUR","created_at":"2026-07-05T01:33:33Z"},{"alias_kind":"pith_short_8","alias_value":"I3LIQ32A","created_at":"2026-07-05T01:33:33Z"}],"graph_snapshots":[{"event_id":"sha256:f5d9a5ae113745627bdaee77e11f0b7a865221171710a671c54a083b091677b0","target":"graph","created_at":"2026-07-05T01:33:33Z","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/1902.05978/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"In the past few years, a lot of work has been done towards reconstructing the 3D facial structure from single images by capitalizing on the power of Deep Convolutional Neural Networks (DCNNs). In the most recent works, differentiable renderers were employed in order to learn the relationship between the facial identity features and the parameters of a 3D morphable model for shape and texture. The texture features either correspond to components of a linear texture space or are learned by auto-encoders directly from in-the-wild images. In all cases, the quality of the facial texture reconstruct","authors_text":"Baris Gecer, Irene Kotsia, Stefanos Zafeiriou, Stylianos Ploumpis","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2019-02-15T19:53:45Z","title":"GANFIT: Generative Adversarial Network Fitting for High Fidelity 3D Face Reconstruction"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1902.05978","kind":"arxiv","version":2},"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":"sha256:787fb4f81c8b6d5448b1e9b4143beedc057bcbb8a37960f913afb9eb47193d8f","target":"record","created_at":"2026-07-05T01:33:33Z","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":"f0aace9f743ae3153b60744a2d46e817a3bfdd8287eb7207fc63cb520a5a2144","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2019-02-15T19:53:45Z","title_canon_sha256":"b56a600556c1beaf26ec83416ab424c5cdc6164b35c0e3617b7a81898992f179"},"schema_version":"1.0","source":{"id":"1902.05978","kind":"arxiv","version":2}},"canonical_sha256":"46d6886f402348c5a2913fdbcba5a492001e4a3d73f28225bce4630f70fc30ab","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"46d6886f402348c5a2913fdbcba5a492001e4a3d73f28225bce4630f70fc30ab","first_computed_at":"2026-07-05T01:33:33.094214Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T01:33:33.094214Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"EKejFp8EQQFLmNpTfkmjj9h0J95AePJz3YpqVas/WBbh+MoBA7bd+eejxJD1JFE6oBOawJROvtLenYa+kDDoCg==","signature_status":"signed_v1","signed_at":"2026-07-05T01:33:33.094709Z","signed_message":"canonical_sha256_bytes"},"source_id":"1902.05978","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:787fb4f81c8b6d5448b1e9b4143beedc057bcbb8a37960f913afb9eb47193d8f","sha256:f5d9a5ae113745627bdaee77e11f0b7a865221171710a671c54a083b091677b0"],"state_sha256":"64090974255db486722d46c7a65262a8e65cb96fe723fb4194e442a58ecce2f1"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"2OItyQuWRjOkz9/PNS9FI9z0YIIojgh0euECpt9Pf4Vgf2HRCnY580R2VkFYlJPrrrG5ROG5Ty2SHmHajx3eCg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-03T20:22:16.309107Z","bundle_sha256":"29f8e07e8e53145a832bbe80d37aae91113ed8808c637c208a34d742b23ed2da"}}