{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:XXA6CMUEEWG5MGH7VWCVU7HKEB","short_pith_number":"pith:XXA6CMUE","canonical_record":{"source":{"id":"2311.16096","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-11-27T18:59:04Z","cross_cats_sorted":["cs.GR"],"title_canon_sha256":"9b8448e76df45fa82a6895cd79cc150341dd738f96fd39e8a8cce1be0ade958c","abstract_canon_sha256":"2b404cfa9ae67ed18184372d8310e3817a4decc08882c8efa9f7ec4626c73580"},"schema_version":"1.0"},"canonical_sha256":"bdc1e13284258dd618ffad855a7cea20549d427cb67761a1e2b1f245fdaa2f6f","source":{"kind":"arxiv","id":"2311.16096","version":4},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2311.16096","created_at":"2026-07-05T08:23:14Z"},{"alias_kind":"arxiv_version","alias_value":"2311.16096v4","created_at":"2026-07-05T08:23:14Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2311.16096","created_at":"2026-07-05T08:23:14Z"},{"alias_kind":"pith_short_12","alias_value":"XXA6CMUEEWG5","created_at":"2026-07-05T08:23:14Z"},{"alias_kind":"pith_short_16","alias_value":"XXA6CMUEEWG5MGH7","created_at":"2026-07-05T08:23:14Z"},{"alias_kind":"pith_short_8","alias_value":"XXA6CMUE","created_at":"2026-07-05T08:23:14Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:XXA6CMUEEWG5MGH7VWCVU7HKEB","target":"record","payload":{"canonical_record":{"source":{"id":"2311.16096","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-11-27T18:59:04Z","cross_cats_sorted":["cs.GR"],"title_canon_sha256":"9b8448e76df45fa82a6895cd79cc150341dd738f96fd39e8a8cce1be0ade958c","abstract_canon_sha256":"2b404cfa9ae67ed18184372d8310e3817a4decc08882c8efa9f7ec4626c73580"},"schema_version":"1.0"},"canonical_sha256":"bdc1e13284258dd618ffad855a7cea20549d427cb67761a1e2b1f245fdaa2f6f","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:23:14.050507Z","signature_b64":"XIn8/cp1NOvcYI6Z1bq6J+AN4fuWUpwfZ2fhR8zTDZ5xC1B37ni6WC0Ojgao8FiIRynKlSwdb/QgTNXf6cQLDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"bdc1e13284258dd618ffad855a7cea20549d427cb67761a1e2b1f245fdaa2f6f","last_reissued_at":"2026-07-05T08:23:14.050045Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:23:14.050045Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2311.16096","source_version":4,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T08:23:14Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"5i8tx4akrZS6RVZhiRVjRhD6KS4bBTjlVePY8BWXTLbqOknCfPnx2A/hfOHrFF+5kiDijyf6DqTWFRnvRMHpBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-16T03:12:17.908689Z"},"content_sha256":"db086719660f24524325708fe25c42bed662d96b25ff85733492a562d31932b6","schema_version":"1.0","event_id":"sha256:db086719660f24524325708fe25c42bed662d96b25ff85733492a562d31932b6"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:XXA6CMUEEWG5MGH7VWCVU7HKEB","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Animatable and Relightable Gaussians for High-fidelity Human Avatar Modeling","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.GR"],"primary_cat":"cs.CV","authors_text":"Lizhen Wang, Shengping Zhang, Yebin Liu, Yipengjing Sun, Zerong Zheng, Zhe Li","submitted_at":"2023-11-27T18:59:04Z","abstract_excerpt":"Modeling animatable human avatars from RGB videos is a long-standing and challenging problem. Recent works usually adopt MLP-based neural radiance fields (NeRF) to represent 3D humans, but it remains difficult for pure MLPs to regress pose-dependent garment details. To this end, we introduce Animatable Gaussians, a new avatar representation that leverages powerful 2D CNNs and 3D Gaussian splatting to create high-fidelity avatars. To associate 3D Gaussians with the animatable avatar, we learn a parametric template from the input videos, and then parameterize the template on two front & back can"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2311.16096","kind":"arxiv","version":4},"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/2311.16096/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":"2026-07-05T08:23:14Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"gYKxTUAaggoZxhGuu9mgC4pQuWS8UbwLugbOpn/FYaNKL3hrElsS9wIPn7Ok+qwFFiZyfKdIVQoBDMi3sJzhCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-16T03:12:17.909271Z"},"content_sha256":"60245cb9fbe50e93e9f5e327ab2e92b43c1a04ad3152e108a6691269138a0148","schema_version":"1.0","event_id":"sha256:60245cb9fbe50e93e9f5e327ab2e92b43c1a04ad3152e108a6691269138a0148"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/XXA6CMUEEWG5MGH7VWCVU7HKEB/bundle.json","state_url":"https://pith.science/pith/XXA6CMUEEWG5MGH7VWCVU7HKEB/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/XXA6CMUEEWG5MGH7VWCVU7HKEB/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":"2026-08-16T03:12:17Z","links":{"resolver":"https://pith.science/pith/XXA6CMUEEWG5MGH7VWCVU7HKEB","bundle":"https://pith.science/pith/XXA6CMUEEWG5MGH7VWCVU7HKEB/bundle.json","state":"https://pith.science/pith/XXA6CMUEEWG5MGH7VWCVU7HKEB/state.json","well_known_bundle":"https://pith.science/.well-known/pith/XXA6CMUEEWG5MGH7VWCVU7HKEB/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:XXA6CMUEEWG5MGH7VWCVU7HKEB","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":"2b404cfa9ae67ed18184372d8310e3817a4decc08882c8efa9f7ec4626c73580","cross_cats_sorted":["cs.GR"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-11-27T18:59:04Z","title_canon_sha256":"9b8448e76df45fa82a6895cd79cc150341dd738f96fd39e8a8cce1be0ade958c"},"schema_version":"1.0","source":{"id":"2311.16096","kind":"arxiv","version":4}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2311.16096","created_at":"2026-07-05T08:23:14Z"},{"alias_kind":"arxiv_version","alias_value":"2311.16096v4","created_at":"2026-07-05T08:23:14Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2311.16096","created_at":"2026-07-05T08:23:14Z"},{"alias_kind":"pith_short_12","alias_value":"XXA6CMUEEWG5","created_at":"2026-07-05T08:23:14Z"},{"alias_kind":"pith_short_16","alias_value":"XXA6CMUEEWG5MGH7","created_at":"2026-07-05T08:23:14Z"},{"alias_kind":"pith_short_8","alias_value":"XXA6CMUE","created_at":"2026-07-05T08:23:14Z"}],"graph_snapshots":[{"event_id":"sha256:60245cb9fbe50e93e9f5e327ab2e92b43c1a04ad3152e108a6691269138a0148","target":"graph","created_at":"2026-07-05T08:23:14Z","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/2311.16096/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Modeling animatable human avatars from RGB videos is a long-standing and challenging problem. Recent works usually adopt MLP-based neural radiance fields (NeRF) to represent 3D humans, but it remains difficult for pure MLPs to regress pose-dependent garment details. To this end, we introduce Animatable Gaussians, a new avatar representation that leverages powerful 2D CNNs and 3D Gaussian splatting to create high-fidelity avatars. To associate 3D Gaussians with the animatable avatar, we learn a parametric template from the input videos, and then parameterize the template on two front & back can","authors_text":"Lizhen Wang, Shengping Zhang, Yebin Liu, Yipengjing Sun, Zerong Zheng, Zhe Li","cross_cats":["cs.GR"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-11-27T18:59:04Z","title":"Animatable and Relightable Gaussians for High-fidelity Human Avatar Modeling"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2311.16096","kind":"arxiv","version":4},"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:db086719660f24524325708fe25c42bed662d96b25ff85733492a562d31932b6","target":"record","created_at":"2026-07-05T08:23:14Z","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":"2b404cfa9ae67ed18184372d8310e3817a4decc08882c8efa9f7ec4626c73580","cross_cats_sorted":["cs.GR"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-11-27T18:59:04Z","title_canon_sha256":"9b8448e76df45fa82a6895cd79cc150341dd738f96fd39e8a8cce1be0ade958c"},"schema_version":"1.0","source":{"id":"2311.16096","kind":"arxiv","version":4}},"canonical_sha256":"bdc1e13284258dd618ffad855a7cea20549d427cb67761a1e2b1f245fdaa2f6f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"bdc1e13284258dd618ffad855a7cea20549d427cb67761a1e2b1f245fdaa2f6f","first_computed_at":"2026-07-05T08:23:14.050045Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:23:14.050045Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"XIn8/cp1NOvcYI6Z1bq6J+AN4fuWUpwfZ2fhR8zTDZ5xC1B37ni6WC0Ojgao8FiIRynKlSwdb/QgTNXf6cQLDA==","signature_status":"signed_v1","signed_at":"2026-07-05T08:23:14.050507Z","signed_message":"canonical_sha256_bytes"},"source_id":"2311.16096","source_kind":"arxiv","source_version":4}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:db086719660f24524325708fe25c42bed662d96b25ff85733492a562d31932b6","sha256:60245cb9fbe50e93e9f5e327ab2e92b43c1a04ad3152e108a6691269138a0148"],"state_sha256":"1e4b95e3280da057fe3d4684b0a34cd5bee47726b895da5a3c3177ad31c0aba3"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"XKhnsUbmUezXKOp2cgDQobrih9V1h60zvBc4dCjjmgPPveMB7FD1nJYlGEE48W+vsNBJ6yswNrmD6m4OBroQBA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-16T03:12:17.914205Z","bundle_sha256":"9248cb5c4318bfbd1d940777fa7a78d595b358f5fe2073db02df8931a30059ac"}}