{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:WDEW7CJBTAG4J4JHLI63YRZE7Q","short_pith_number":"pith:WDEW7CJB","canonical_record":{"source":{"id":"1905.00307","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-05-01T13:31:01Z","cross_cats_sorted":[],"title_canon_sha256":"61591a1a8fe4c1ce727de679622f1c45bc82128ef7d0da410c63e7548ec433aa","abstract_canon_sha256":"1267b76523b37ffe65a9b0a07e2c35d1edefef7f245473373aee28df4854be82"},"schema_version":"1.0"},"canonical_sha256":"b0c96f8921980dc4f1275a3dbc4724fc258298b46b54712eb9699bef46c10a97","source":{"kind":"arxiv","id":"1905.00307","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1905.00307","created_at":"2026-05-17T23:46:35Z"},{"alias_kind":"arxiv_version","alias_value":"1905.00307v2","created_at":"2026-05-17T23:46:35Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1905.00307","created_at":"2026-05-17T23:46:35Z"},{"alias_kind":"pith_short_12","alias_value":"WDEW7CJBTAG4","created_at":"2026-05-18T12:33:30Z"},{"alias_kind":"pith_short_16","alias_value":"WDEW7CJBTAG4J4JH","created_at":"2026-05-18T12:33:30Z"},{"alias_kind":"pith_short_8","alias_value":"WDEW7CJB","created_at":"2026-05-18T12:33:30Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:WDEW7CJBTAG4J4JHLI63YRZE7Q","target":"record","payload":{"canonical_record":{"source":{"id":"1905.00307","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-05-01T13:31:01Z","cross_cats_sorted":[],"title_canon_sha256":"61591a1a8fe4c1ce727de679622f1c45bc82128ef7d0da410c63e7548ec433aa","abstract_canon_sha256":"1267b76523b37ffe65a9b0a07e2c35d1edefef7f245473373aee28df4854be82"},"schema_version":"1.0"},"canonical_sha256":"b0c96f8921980dc4f1275a3dbc4724fc258298b46b54712eb9699bef46c10a97","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:46:35.691957Z","signature_b64":"LkdO7dhR/dKs3NkWhir4DbKoc4KCpNDxk3a5ThzycdXtck3Ie29bVcueiLBI2NFuey/JCDj/l+llAuK/hI4aBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b0c96f8921980dc4f1275a3dbc4724fc258298b46b54712eb9699bef46c10a97","last_reissued_at":"2026-05-17T23:46:35.691292Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:46:35.691292Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1905.00307","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":"2026-05-17T23:46:35Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Dd4qOdZAJeaD66rzwhfSV8nHwc7uaFiyd5/ds/xvsU6a80vlbpHKgb75C7ItvFUoHRv0rUmC8UEKSHIMd7ToBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-31T00:15:45.004401Z"},"content_sha256":"c44b26638c31189fb3a0f69eef4df5f9122bdc45440faf9b84d5ce68d87f8889","schema_version":"1.0","event_id":"sha256:c44b26638c31189fb3a0f69eef4df5f9122bdc45440faf9b84d5ce68d87f8889"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:WDEW7CJBTAG4J4JHLI63YRZE7Q","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"3DFaceGAN: Adversarial Nets for 3D Face Representation, Generation, and Translation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Athanasios Papaioannou, Mihalis Nicolaou, Stefanos Zafeiriou, Stylianos Moschoglou, Stylianos Ploumpis","submitted_at":"2019-05-01T13:31:01Z","abstract_excerpt":"Over the past few years, Generative Adversarial Networks (GANs) have garnered increased interest among researchers in Computer Vision, with applications including, but not limited to, image generation, translation, imputation, and super-resolution. Nevertheless, no GAN-based method has been proposed in the literature that can successfully represent, generate or translate 3D facial shapes (meshes). This can be primarily attributed to two facts, namely that (a) publicly available 3D face databases are scarce as well as limited in terms of sample size and variability (e.g., few subjects, little d"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1905.00307","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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:46:35Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"uOQGjNOV7VlPCr1i4dRalP/w+Cfv6N63NMAUkhMjXxd9U5ePThE4lu0+BSjTSG1265dZ3WRnVpoR1p4+V41FAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-31T00:15:45.004877Z"},"content_sha256":"99f17aadeb46bd86f67b7e6397fdddd9f24d4da9d3143c02a3ba29e8ea7bbe30","schema_version":"1.0","event_id":"sha256:99f17aadeb46bd86f67b7e6397fdddd9f24d4da9d3143c02a3ba29e8ea7bbe30"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/WDEW7CJBTAG4J4JHLI63YRZE7Q/bundle.json","state_url":"https://pith.science/pith/WDEW7CJBTAG4J4JHLI63YRZE7Q/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/WDEW7CJBTAG4J4JHLI63YRZE7Q/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":"2026-05-31T00:15:45Z","links":{"resolver":"https://pith.science/pith/WDEW7CJBTAG4J4JHLI63YRZE7Q","bundle":"https://pith.science/pith/WDEW7CJBTAG4J4JHLI63YRZE7Q/bundle.json","state":"https://pith.science/pith/WDEW7CJBTAG4J4JHLI63YRZE7Q/state.json","well_known_bundle":"https://pith.science/.well-known/pith/WDEW7CJBTAG4J4JHLI63YRZE7Q/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:WDEW7CJBTAG4J4JHLI63YRZE7Q","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":"1267b76523b37ffe65a9b0a07e2c35d1edefef7f245473373aee28df4854be82","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-05-01T13:31:01Z","title_canon_sha256":"61591a1a8fe4c1ce727de679622f1c45bc82128ef7d0da410c63e7548ec433aa"},"schema_version":"1.0","source":{"id":"1905.00307","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1905.00307","created_at":"2026-05-17T23:46:35Z"},{"alias_kind":"arxiv_version","alias_value":"1905.00307v2","created_at":"2026-05-17T23:46:35Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1905.00307","created_at":"2026-05-17T23:46:35Z"},{"alias_kind":"pith_short_12","alias_value":"WDEW7CJBTAG4","created_at":"2026-05-18T12:33:30Z"},{"alias_kind":"pith_short_16","alias_value":"WDEW7CJBTAG4J4JH","created_at":"2026-05-18T12:33:30Z"},{"alias_kind":"pith_short_8","alias_value":"WDEW7CJB","created_at":"2026-05-18T12:33:30Z"}],"graph_snapshots":[{"event_id":"sha256:99f17aadeb46bd86f67b7e6397fdddd9f24d4da9d3143c02a3ba29e8ea7bbe30","target":"graph","created_at":"2026-05-17T23:46:35Z","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":"Over the past few years, Generative Adversarial Networks (GANs) have garnered increased interest among researchers in Computer Vision, with applications including, but not limited to, image generation, translation, imputation, and super-resolution. Nevertheless, no GAN-based method has been proposed in the literature that can successfully represent, generate or translate 3D facial shapes (meshes). This can be primarily attributed to two facts, namely that (a) publicly available 3D face databases are scarce as well as limited in terms of sample size and variability (e.g., few subjects, little d","authors_text":"Athanasios Papaioannou, Mihalis Nicolaou, Stefanos Zafeiriou, Stylianos Moschoglou, Stylianos Ploumpis","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-05-01T13:31:01Z","title":"3DFaceGAN: Adversarial Nets for 3D Face Representation, Generation, and Translation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1905.00307","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:c44b26638c31189fb3a0f69eef4df5f9122bdc45440faf9b84d5ce68d87f8889","target":"record","created_at":"2026-05-17T23:46:35Z","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":"1267b76523b37ffe65a9b0a07e2c35d1edefef7f245473373aee28df4854be82","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-05-01T13:31:01Z","title_canon_sha256":"61591a1a8fe4c1ce727de679622f1c45bc82128ef7d0da410c63e7548ec433aa"},"schema_version":"1.0","source":{"id":"1905.00307","kind":"arxiv","version":2}},"canonical_sha256":"b0c96f8921980dc4f1275a3dbc4724fc258298b46b54712eb9699bef46c10a97","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"b0c96f8921980dc4f1275a3dbc4724fc258298b46b54712eb9699bef46c10a97","first_computed_at":"2026-05-17T23:46:35.691292Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:46:35.691292Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"LkdO7dhR/dKs3NkWhir4DbKoc4KCpNDxk3a5ThzycdXtck3Ie29bVcueiLBI2NFuey/JCDj/l+llAuK/hI4aBQ==","signature_status":"signed_v1","signed_at":"2026-05-17T23:46:35.691957Z","signed_message":"canonical_sha256_bytes"},"source_id":"1905.00307","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c44b26638c31189fb3a0f69eef4df5f9122bdc45440faf9b84d5ce68d87f8889","sha256:99f17aadeb46bd86f67b7e6397fdddd9f24d4da9d3143c02a3ba29e8ea7bbe30"],"state_sha256":"b24264cdd2342f5bc6d50519b6849afefdf9bf10b8d9d1376c3c1c4dfa52484c"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"q7L2r1b06XoHVcztL5qbddXXjI/A7nMzi4kW4GYsJxzOwuqI84sNw3sfj8NS05ObJegmGNstJAmVF2SWOTYwBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-31T00:15:45.006964Z","bundle_sha256":"0f6f3302c4799f2637bba78b010ae381c6cad01e6f16d85be30644147529629e"}}