{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2020:4K4R32BL637LHQRVFMUF2DW3FG","short_pith_number":"pith:4K4R32BL","canonical_record":{"source":{"id":"2003.06211","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2020-03-13T11:52:53Z","cross_cats_sorted":[],"title_canon_sha256":"0cc882594196ac617d1ef7dbce928008ca9a43cd0190894d3bac381c446d6d2f","abstract_canon_sha256":"a9c49d3862b6eb4b575f26616f6e87b98f3567ba63fa24f5ae6cd855a4d9108f"},"schema_version":"1.0"},"canonical_sha256":"e2b91de82bf6feb3c2352b285d0edb2987d8ff59038485d60a7aefeb2471c470","source":{"kind":"arxiv","id":"2003.06211","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2003.06211","created_at":"2026-07-05T00:50:45Z"},{"alias_kind":"arxiv_version","alias_value":"2003.06211v2","created_at":"2026-07-05T00:50:45Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2003.06211","created_at":"2026-07-05T00:50:45Z"},{"alias_kind":"pith_short_12","alias_value":"4K4R32BL637L","created_at":"2026-07-05T00:50:45Z"},{"alias_kind":"pith_short_16","alias_value":"4K4R32BL637LHQRV","created_at":"2026-07-05T00:50:45Z"},{"alias_kind":"pith_short_8","alias_value":"4K4R32BL","created_at":"2026-07-05T00:50:45Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2020:4K4R32BL637LHQRVFMUF2DW3FG","target":"record","payload":{"canonical_record":{"source":{"id":"2003.06211","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2020-03-13T11:52:53Z","cross_cats_sorted":[],"title_canon_sha256":"0cc882594196ac617d1ef7dbce928008ca9a43cd0190894d3bac381c446d6d2f","abstract_canon_sha256":"a9c49d3862b6eb4b575f26616f6e87b98f3567ba63fa24f5ae6cd855a4d9108f"},"schema_version":"1.0"},"canonical_sha256":"e2b91de82bf6feb3c2352b285d0edb2987d8ff59038485d60a7aefeb2471c470","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:50:45.557722Z","signature_b64":"mLYgSMISLoiSgALWovFTk4AfPP33p9q5rmPrbnc84mWuu3sLADHjpDl/B55W0PA9ZaDubq6gwFdMAzHgqHBrAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e2b91de82bf6feb3c2352b285d0edb2987d8ff59038485d60a7aefeb2471c470","last_reissued_at":"2026-07-05T00:50:45.557306Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:50:45.557306Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2003.06211","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":"2026-07-05T00:50:45Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"t7R+3/+2lRV4NrGRhOeYHq5u68qrP+gkMPaImthbA3kX0jhMeAoMO7fUx8NmUk2neF6pKaTWdibx6GxoUE/rAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-11T21:13:22.791287Z"},"content_sha256":"759357a72a49868c26659de9817e5b44487d5726c06648d7bebef45a52f98a88","schema_version":"1.0","event_id":"sha256:759357a72a49868c26659de9817e5b44487d5726c06648d7bebef45a52f98a88"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2020:4K4R32BL637LHQRVFMUF2DW3FG","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"High-Accuracy Facial Depth Models derived from 3D Synthetic Data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"eess.IV","authors_text":"Faisal Khan, Hossein Javidnia, Michael Schukat, Peter Corcoran, Shubhajit Basak","submitted_at":"2020-03-13T11:52:53Z","abstract_excerpt":"In this paper, we explore how synthetically generated 3D face models can be used to construct a high accuracy ground truth for depth. This allows us to train the Convolutional Neural Networks (CNN) to solve facial depth estimation problems. These models provide sophisticated controls over image variations including pose, illumination, facial expressions and camera position. 2D training samples can be rendered from these models, typically in RGB format, together with depth information. Using synthetic facial animations, a dynamic facial expression or facial action data can be rendered for a seq"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2003.06211","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/2003.06211/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":"2026-07-05T00:50:45Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"RUA2alenav6Hozjnks+PxzjiLERZPdkERObu64qmdR2AliaCgbWoOMvER1mrp/U90q+zMe4VwFvd2GnZ6fcQDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-11T21:13:22.791672Z"},"content_sha256":"013ae1389b9221dbc57f8fdf904a0e0f59bc3306d5501356793815aa2d28c425","schema_version":"1.0","event_id":"sha256:013ae1389b9221dbc57f8fdf904a0e0f59bc3306d5501356793815aa2d28c425"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/4K4R32BL637LHQRVFMUF2DW3FG/bundle.json","state_url":"https://pith.science/pith/4K4R32BL637LHQRVFMUF2DW3FG/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/4K4R32BL637LHQRVFMUF2DW3FG/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":"2026-08-11T21:13:22Z","links":{"resolver":"https://pith.science/pith/4K4R32BL637LHQRVFMUF2DW3FG","bundle":"https://pith.science/pith/4K4R32BL637LHQRVFMUF2DW3FG/bundle.json","state":"https://pith.science/pith/4K4R32BL637LHQRVFMUF2DW3FG/state.json","well_known_bundle":"https://pith.science/.well-known/pith/4K4R32BL637LHQRVFMUF2DW3FG/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:4K4R32BL637LHQRVFMUF2DW3FG","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":"a9c49d3862b6eb4b575f26616f6e87b98f3567ba63fa24f5ae6cd855a4d9108f","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2020-03-13T11:52:53Z","title_canon_sha256":"0cc882594196ac617d1ef7dbce928008ca9a43cd0190894d3bac381c446d6d2f"},"schema_version":"1.0","source":{"id":"2003.06211","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2003.06211","created_at":"2026-07-05T00:50:45Z"},{"alias_kind":"arxiv_version","alias_value":"2003.06211v2","created_at":"2026-07-05T00:50:45Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2003.06211","created_at":"2026-07-05T00:50:45Z"},{"alias_kind":"pith_short_12","alias_value":"4K4R32BL637L","created_at":"2026-07-05T00:50:45Z"},{"alias_kind":"pith_short_16","alias_value":"4K4R32BL637LHQRV","created_at":"2026-07-05T00:50:45Z"},{"alias_kind":"pith_short_8","alias_value":"4K4R32BL","created_at":"2026-07-05T00:50:45Z"}],"graph_snapshots":[{"event_id":"sha256:013ae1389b9221dbc57f8fdf904a0e0f59bc3306d5501356793815aa2d28c425","target":"graph","created_at":"2026-07-05T00:50:45Z","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/2003.06211/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"In this paper, we explore how synthetically generated 3D face models can be used to construct a high accuracy ground truth for depth. This allows us to train the Convolutional Neural Networks (CNN) to solve facial depth estimation problems. These models provide sophisticated controls over image variations including pose, illumination, facial expressions and camera position. 2D training samples can be rendered from these models, typically in RGB format, together with depth information. Using synthetic facial animations, a dynamic facial expression or facial action data can be rendered for a seq","authors_text":"Faisal Khan, Hossein Javidnia, Michael Schukat, Peter Corcoran, Shubhajit Basak","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2020-03-13T11:52:53Z","title":"High-Accuracy Facial Depth Models derived from 3D Synthetic Data"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2003.06211","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":"sha256:759357a72a49868c26659de9817e5b44487d5726c06648d7bebef45a52f98a88","target":"record","created_at":"2026-07-05T00:50:45Z","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":"a9c49d3862b6eb4b575f26616f6e87b98f3567ba63fa24f5ae6cd855a4d9108f","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2020-03-13T11:52:53Z","title_canon_sha256":"0cc882594196ac617d1ef7dbce928008ca9a43cd0190894d3bac381c446d6d2f"},"schema_version":"1.0","source":{"id":"2003.06211","kind":"arxiv","version":2}},"canonical_sha256":"e2b91de82bf6feb3c2352b285d0edb2987d8ff59038485d60a7aefeb2471c470","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"e2b91de82bf6feb3c2352b285d0edb2987d8ff59038485d60a7aefeb2471c470","first_computed_at":"2026-07-05T00:50:45.557306Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T00:50:45.557306Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"mLYgSMISLoiSgALWovFTk4AfPP33p9q5rmPrbnc84mWuu3sLADHjpDl/B55W0PA9ZaDubq6gwFdMAzHgqHBrAw==","signature_status":"signed_v1","signed_at":"2026-07-05T00:50:45.557722Z","signed_message":"canonical_sha256_bytes"},"source_id":"2003.06211","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:759357a72a49868c26659de9817e5b44487d5726c06648d7bebef45a52f98a88","sha256:013ae1389b9221dbc57f8fdf904a0e0f59bc3306d5501356793815aa2d28c425"],"state_sha256":"dd683fc53589837c57d2325e9ba535634b549a7e72648896114da6f92656126b"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"9JYV/cO4KS3a8QqinP0j4rue+AKHSrnPqCQRB0RiOgJGLFGp9pIZM9X0luvFjvUsqhtHkgUpapIH2NEX87lCDA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-11T21:13:22.794203Z","bundle_sha256":"c647cc3a50471e9fd148eaf8080aad3a32e49f1003e3c8ff09cba8fdcc063385"}}