{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2016:LKK5O4XR54MTJLJ7TGQZG5RVR3","short_pith_number":"pith:LKK5O4XR","canonical_record":{"source":{"id":"1609.04387","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-09-14T19:47:12Z","cross_cats_sorted":[],"title_canon_sha256":"103fd6b1826fa0295bc4400bf04e2aae025cb174104ad7892160d46f32a3323c","abstract_canon_sha256":"9b0f54c8b4f4bb1e4f23054e79703226373195b24e82475aabf90ebc5de6b945"},"schema_version":"1.0"},"canonical_sha256":"5a95d772f1ef1934ad3f99a19376358ef42f060ebf2b4c3319bd1b98d519c062","source":{"kind":"arxiv","id":"1609.04387","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1609.04387","created_at":"2026-05-18T01:03:56Z"},{"alias_kind":"arxiv_version","alias_value":"1609.04387v2","created_at":"2026-05-18T01:03:56Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1609.04387","created_at":"2026-05-18T01:03:56Z"},{"alias_kind":"pith_short_12","alias_value":"LKK5O4XR54MT","created_at":"2026-05-18T12:30:29Z"},{"alias_kind":"pith_short_16","alias_value":"LKK5O4XR54MTJLJ7","created_at":"2026-05-18T12:30:29Z"},{"alias_kind":"pith_short_8","alias_value":"LKK5O4XR","created_at":"2026-05-18T12:30:29Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2016:LKK5O4XR54MTJLJ7TGQZG5RVR3","target":"record","payload":{"canonical_record":{"source":{"id":"1609.04387","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-09-14T19:47:12Z","cross_cats_sorted":[],"title_canon_sha256":"103fd6b1826fa0295bc4400bf04e2aae025cb174104ad7892160d46f32a3323c","abstract_canon_sha256":"9b0f54c8b4f4bb1e4f23054e79703226373195b24e82475aabf90ebc5de6b945"},"schema_version":"1.0"},"canonical_sha256":"5a95d772f1ef1934ad3f99a19376358ef42f060ebf2b4c3319bd1b98d519c062","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:03:56.398863Z","signature_b64":"6KNAaa3GwH0nG4AKF9cUuR01jm0mpqIWwrGIJN01LUTu/74g8brIoOlmevAhPNld6NQTXfzxxHcPlyHUru6/DA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5a95d772f1ef1934ad3f99a19376358ef42f060ebf2b4c3319bd1b98d519c062","last_reissued_at":"2026-05-18T01:03:56.398155Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:03:56.398155Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1609.04387","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":"2026-05-18T01:03:56Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"QuhOTKEmWLwzvzr8ogyUdAyeLkOKNiSuSAXHFriiTMf/mZjyJKkFbrCYEIdzPMaNO/j74MbUxEIuqoIudF23BA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-01T16:33:34.695721Z"},"content_sha256":"69388a3a3150b18824ed4bbd555525929415201ed2993466bdb754e391027733","schema_version":"1.0","event_id":"sha256:69388a3a3150b18824ed4bbd555525929415201ed2993466bdb754e391027733"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2016:LKK5O4XR54MTJLJ7TGQZG5RVR3","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"3D Face Reconstruction by Learning from Synthetic Data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Elad Richardson, Matan Sela, Ron Kimmel","submitted_at":"2016-09-14T19:47:12Z","abstract_excerpt":"Fast and robust three-dimensional reconstruction of facial geometric structure from a single image is a challenging task with numerous applications. Here, we introduce a learning-based approach for reconstructing a three-dimensional face from a single image. Recent face recovery methods rely on accurate localization of key characteristic points. In contrast, the proposed approach is based on a Convolutional-Neural-Network (CNN) which extracts the face geometry directly from its image. Although such deep architectures outperform other models in complex computer vision problems, training them pr"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1609.04387","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":"2026-05-18T01:03:56Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"s57Y2s0cxAu1Rh3hC03n40bfrlEH1HVQdTUyn4LljxRyfc8uzGSPIq11xXwSQtO1S2+q4FQWuj+DZ0KSF4reCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-01T16:33:34.696068Z"},"content_sha256":"edfb26aafb69c44998efd8452480cd8ed152ec8a6445c2c9f83e731f505777a3","schema_version":"1.0","event_id":"sha256:edfb26aafb69c44998efd8452480cd8ed152ec8a6445c2c9f83e731f505777a3"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/LKK5O4XR54MTJLJ7TGQZG5RVR3/bundle.json","state_url":"https://pith.science/pith/LKK5O4XR54MTJLJ7TGQZG5RVR3/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/LKK5O4XR54MTJLJ7TGQZG5RVR3/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":"2026-06-01T16:33:34Z","links":{"resolver":"https://pith.science/pith/LKK5O4XR54MTJLJ7TGQZG5RVR3","bundle":"https://pith.science/pith/LKK5O4XR54MTJLJ7TGQZG5RVR3/bundle.json","state":"https://pith.science/pith/LKK5O4XR54MTJLJ7TGQZG5RVR3/state.json","well_known_bundle":"https://pith.science/.well-known/pith/LKK5O4XR54MTJLJ7TGQZG5RVR3/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2016:LKK5O4XR54MTJLJ7TGQZG5RVR3","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":"9b0f54c8b4f4bb1e4f23054e79703226373195b24e82475aabf90ebc5de6b945","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-09-14T19:47:12Z","title_canon_sha256":"103fd6b1826fa0295bc4400bf04e2aae025cb174104ad7892160d46f32a3323c"},"schema_version":"1.0","source":{"id":"1609.04387","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1609.04387","created_at":"2026-05-18T01:03:56Z"},{"alias_kind":"arxiv_version","alias_value":"1609.04387v2","created_at":"2026-05-18T01:03:56Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1609.04387","created_at":"2026-05-18T01:03:56Z"},{"alias_kind":"pith_short_12","alias_value":"LKK5O4XR54MT","created_at":"2026-05-18T12:30:29Z"},{"alias_kind":"pith_short_16","alias_value":"LKK5O4XR54MTJLJ7","created_at":"2026-05-18T12:30:29Z"},{"alias_kind":"pith_short_8","alias_value":"LKK5O4XR","created_at":"2026-05-18T12:30:29Z"}],"graph_snapshots":[{"event_id":"sha256:edfb26aafb69c44998efd8452480cd8ed152ec8a6445c2c9f83e731f505777a3","target":"graph","created_at":"2026-05-18T01:03:56Z","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":"Fast and robust three-dimensional reconstruction of facial geometric structure from a single image is a challenging task with numerous applications. Here, we introduce a learning-based approach for reconstructing a three-dimensional face from a single image. Recent face recovery methods rely on accurate localization of key characteristic points. In contrast, the proposed approach is based on a Convolutional-Neural-Network (CNN) which extracts the face geometry directly from its image. Although such deep architectures outperform other models in complex computer vision problems, training them pr","authors_text":"Elad Richardson, Matan Sela, Ron Kimmel","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-09-14T19:47:12Z","title":"3D Face Reconstruction by Learning from Synthetic Data"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1609.04387","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":"sha256:69388a3a3150b18824ed4bbd555525929415201ed2993466bdb754e391027733","target":"record","created_at":"2026-05-18T01:03:56Z","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":"9b0f54c8b4f4bb1e4f23054e79703226373195b24e82475aabf90ebc5de6b945","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-09-14T19:47:12Z","title_canon_sha256":"103fd6b1826fa0295bc4400bf04e2aae025cb174104ad7892160d46f32a3323c"},"schema_version":"1.0","source":{"id":"1609.04387","kind":"arxiv","version":2}},"canonical_sha256":"5a95d772f1ef1934ad3f99a19376358ef42f060ebf2b4c3319bd1b98d519c062","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"5a95d772f1ef1934ad3f99a19376358ef42f060ebf2b4c3319bd1b98d519c062","first_computed_at":"2026-05-18T01:03:56.398155Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T01:03:56.398155Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"6KNAaa3GwH0nG4AKF9cUuR01jm0mpqIWwrGIJN01LUTu/74g8brIoOlmevAhPNld6NQTXfzxxHcPlyHUru6/DA==","signature_status":"signed_v1","signed_at":"2026-05-18T01:03:56.398863Z","signed_message":"canonical_sha256_bytes"},"source_id":"1609.04387","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:69388a3a3150b18824ed4bbd555525929415201ed2993466bdb754e391027733","sha256:edfb26aafb69c44998efd8452480cd8ed152ec8a6445c2c9f83e731f505777a3"],"state_sha256":"3dd7325795f7d9550ad020ba49daf36253696bdc99a4eee95f689f61a2427bca"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"hAX1HvwvleQ/e+FputnTfOzPe8gnjl4M8sFHNmtImGUL57PvvcNt79DCmT5cY7oXokaoho/gLXv4K1D72F18AA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-01T16:33:34.698011Z","bundle_sha256":"affbf9a708b53508b8f38d0ebcf26ef4a8768b040e78b72f35e682a9c275db71"}}