{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:3ZVL2CCOJ7UXO7H3DIVU7KHNFM","short_pith_number":"pith:3ZVL2CCO","canonical_record":{"source":{"id":"2310.17519","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2023-10-26T16:13:00Z","cross_cats_sorted":[],"title_canon_sha256":"f61a81ccd6050061fdb6922bb6a65919ba693abe65330f3b1ed1882d79b427e8","abstract_canon_sha256":"a493d1dec8d1da752387b7b78810dadf739d223e68f79a6419c513f44a4daa59"},"schema_version":"1.0"},"canonical_sha256":"de6abd084e4fe9777cfb1a2b4fa8ed2b0ec4ac68fae2a7626910b18c04310893","source":{"kind":"arxiv","id":"2310.17519","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2310.17519","created_at":"2026-07-05T07:05:50Z"},{"alias_kind":"arxiv_version","alias_value":"2310.17519v2","created_at":"2026-07-05T07:05:50Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.17519","created_at":"2026-07-05T07:05:50Z"},{"alias_kind":"pith_short_12","alias_value":"3ZVL2CCOJ7UX","created_at":"2026-07-05T07:05:50Z"},{"alias_kind":"pith_short_16","alias_value":"3ZVL2CCOJ7UXO7H3","created_at":"2026-07-05T07:05:50Z"},{"alias_kind":"pith_short_8","alias_value":"3ZVL2CCO","created_at":"2026-07-05T07:05:50Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:3ZVL2CCOJ7UXO7H3DIVU7KHNFM","target":"record","payload":{"canonical_record":{"source":{"id":"2310.17519","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2023-10-26T16:13:00Z","cross_cats_sorted":[],"title_canon_sha256":"f61a81ccd6050061fdb6922bb6a65919ba693abe65330f3b1ed1882d79b427e8","abstract_canon_sha256":"a493d1dec8d1da752387b7b78810dadf739d223e68f79a6419c513f44a4daa59"},"schema_version":"1.0"},"canonical_sha256":"de6abd084e4fe9777cfb1a2b4fa8ed2b0ec4ac68fae2a7626910b18c04310893","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:05:50.001205Z","signature_b64":"P6/zKCPWanCOt6qMNGKrokoUYjg6dxq4s5Cewvz03eO+gaRP7ZH8EvjLt2wHfOXeXPr50N3w4NDcrIoOOTBXCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"de6abd084e4fe9777cfb1a2b4fa8ed2b0ec4ac68fae2a7626910b18c04310893","last_reissued_at":"2026-07-05T07:05:50.000748Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:05:50.000748Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2310.17519","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":"2026-07-05T07:05:50Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"tqA9uJsWwgQAD1VA/neBfhgcNxUBMu8DGEGPTJYhhLRRdfcAFV0sBSy2BQYJRu+3AlvdkGiKDvS7K6LsMLbwCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-28T22:11:22.707998Z"},"content_sha256":"41b97800f4c833ef16cc93d198b972bd260c8ae32d8dad5f7411adb010a831f0","schema_version":"1.0","event_id":"sha256:41b97800f4c833ef16cc93d198b972bd260c8ae32d8dad5f7411adb010a831f0"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:3ZVL2CCOJ7UXO7H3DIVU7KHNFM","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"FLARE: Fast Learning of Animatable and Relightable Mesh Avatars","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Michael J. Black, Otmar Hilliges, Shrisha Bharadwaj, Victoria Fernandez-Abrevaya, Yufeng Zheng","submitted_at":"2023-10-26T16:13:00Z","abstract_excerpt":"Our goal is to efficiently learn personalized animatable 3D head avatars from videos that are geometrically accurate, realistic, relightable, and compatible with current rendering systems. While 3D meshes enable efficient processing and are highly portable, they lack realism in terms of shape and appearance. Neural representations, on the other hand, are realistic but lack compatibility and are slow to train and render. Our key insight is that it is possible to efficiently learn high-fidelity 3D mesh representations via differentiable rendering by exploiting highly-optimized methods from tradi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.17519","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/2310.17519/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":"2026-07-05T07:05:50Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"jBIovCAyJ1gdFAVCuPLwwmypVLF7UmSZSFlxgunAH/TBX/odITtxJhc8vImL9sU7b0MZX85sDFRzKL7ltacAAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-28T22:11:22.708389Z"},"content_sha256":"b1c7f7e93af69a5a2f869ba178447b96838c1ccc36d5988dedef5f112c73164d","schema_version":"1.0","event_id":"sha256:b1c7f7e93af69a5a2f869ba178447b96838c1ccc36d5988dedef5f112c73164d"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/3ZVL2CCOJ7UXO7H3DIVU7KHNFM/bundle.json","state_url":"https://pith.science/pith/3ZVL2CCOJ7UXO7H3DIVU7KHNFM/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/3ZVL2CCOJ7UXO7H3DIVU7KHNFM/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":"2026-07-28T22:11:22Z","links":{"resolver":"https://pith.science/pith/3ZVL2CCOJ7UXO7H3DIVU7KHNFM","bundle":"https://pith.science/pith/3ZVL2CCOJ7UXO7H3DIVU7KHNFM/bundle.json","state":"https://pith.science/pith/3ZVL2CCOJ7UXO7H3DIVU7KHNFM/state.json","well_known_bundle":"https://pith.science/.well-known/pith/3ZVL2CCOJ7UXO7H3DIVU7KHNFM/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:3ZVL2CCOJ7UXO7H3DIVU7KHNFM","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":"a493d1dec8d1da752387b7b78810dadf739d223e68f79a6419c513f44a4daa59","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2023-10-26T16:13:00Z","title_canon_sha256":"f61a81ccd6050061fdb6922bb6a65919ba693abe65330f3b1ed1882d79b427e8"},"schema_version":"1.0","source":{"id":"2310.17519","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2310.17519","created_at":"2026-07-05T07:05:50Z"},{"alias_kind":"arxiv_version","alias_value":"2310.17519v2","created_at":"2026-07-05T07:05:50Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.17519","created_at":"2026-07-05T07:05:50Z"},{"alias_kind":"pith_short_12","alias_value":"3ZVL2CCOJ7UX","created_at":"2026-07-05T07:05:50Z"},{"alias_kind":"pith_short_16","alias_value":"3ZVL2CCOJ7UXO7H3","created_at":"2026-07-05T07:05:50Z"},{"alias_kind":"pith_short_8","alias_value":"3ZVL2CCO","created_at":"2026-07-05T07:05:50Z"}],"graph_snapshots":[{"event_id":"sha256:b1c7f7e93af69a5a2f869ba178447b96838c1ccc36d5988dedef5f112c73164d","target":"graph","created_at":"2026-07-05T07:05:50Z","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/2310.17519/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Our goal is to efficiently learn personalized animatable 3D head avatars from videos that are geometrically accurate, realistic, relightable, and compatible with current rendering systems. While 3D meshes enable efficient processing and are highly portable, they lack realism in terms of shape and appearance. Neural representations, on the other hand, are realistic but lack compatibility and are slow to train and render. Our key insight is that it is possible to efficiently learn high-fidelity 3D mesh representations via differentiable rendering by exploiting highly-optimized methods from tradi","authors_text":"Michael J. Black, Otmar Hilliges, Shrisha Bharadwaj, Victoria Fernandez-Abrevaya, Yufeng Zheng","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2023-10-26T16:13:00Z","title":"FLARE: Fast Learning of Animatable and Relightable Mesh Avatars"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.17519","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":"sha256:41b97800f4c833ef16cc93d198b972bd260c8ae32d8dad5f7411adb010a831f0","target":"record","created_at":"2026-07-05T07:05:50Z","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":"a493d1dec8d1da752387b7b78810dadf739d223e68f79a6419c513f44a4daa59","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2023-10-26T16:13:00Z","title_canon_sha256":"f61a81ccd6050061fdb6922bb6a65919ba693abe65330f3b1ed1882d79b427e8"},"schema_version":"1.0","source":{"id":"2310.17519","kind":"arxiv","version":2}},"canonical_sha256":"de6abd084e4fe9777cfb1a2b4fa8ed2b0ec4ac68fae2a7626910b18c04310893","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"de6abd084e4fe9777cfb1a2b4fa8ed2b0ec4ac68fae2a7626910b18c04310893","first_computed_at":"2026-07-05T07:05:50.000748Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:05:50.000748Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"P6/zKCPWanCOt6qMNGKrokoUYjg6dxq4s5Cewvz03eO+gaRP7ZH8EvjLt2wHfOXeXPr50N3w4NDcrIoOOTBXCw==","signature_status":"signed_v1","signed_at":"2026-07-05T07:05:50.001205Z","signed_message":"canonical_sha256_bytes"},"source_id":"2310.17519","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:41b97800f4c833ef16cc93d198b972bd260c8ae32d8dad5f7411adb010a831f0","sha256:b1c7f7e93af69a5a2f869ba178447b96838c1ccc36d5988dedef5f112c73164d"],"state_sha256":"a87547c8cd305cfe05577f36a46e68ba6a19ae90996b37e7637af3fe201a2962"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"pP9SCIrx9TEvizD7f8rWCMOPm5QkMGzi6sfqYKIMQ3LaFpejJ7nBJG5+vXSazNr7bAMCw1tQEqi0YRVhBp3vBA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-28T22:11:22.711231Z","bundle_sha256":"6f69c13e8ab442a4e73ea6b84173c4f613667234feec0a971542b9b602e91f8d"}}