{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:UNL3W3TES4CEMN3WI5RQHMYL5Y","short_pith_number":"pith:UNL3W3TE","schema_version":"1.0","canonical_sha256":"a357bb6e649704463776476303b30bee003b1d99e6f837435d2887b69164bf59","source":{"kind":"arxiv","id":"2112.06904","version":3},"attestation_state":"computed","paper":{"title":"HVH: Learning a Hybrid Neural Volumetric Representation for Dynamic Hair Performance Capture","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.GR"],"primary_cat":"cs.CV","authors_text":"Christoph Lassner, Giljoo Nam, Jessica Hodgins, Michael Zollhoefer, Stephen Lombardi, Tuur Stuyck, Ziyan Wang","submitted_at":"2021-12-13T18:57:50Z","abstract_excerpt":"Capturing and rendering life-like hair is particularly challenging due to its fine geometric structure, the complex physical interaction and its non-trivial visual appearance.Yet, hair is a critical component for believable avatars. In this paper, we address the aforementioned problems: 1) we use a novel, volumetric hair representation that is com-posed of thousands of primitives. Each primitive can be rendered efficiently, yet realistically, by building on the latest advances in neural rendering. 2) To have a reliable control signal, we present a novel way of tracking hair on the strand level"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2112.06904","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2021-12-13T18:57:50Z","cross_cats_sorted":["cs.GR"],"title_canon_sha256":"1844c7962285268c39167429bb8367d1a180826ce39fcd2f8df4cf470ed09154","abstract_canon_sha256":"b99f79e9182814afd6fec4b182f55f6d7f156f8e55e1292da3e203b3b7a29c02"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:42:06.543345Z","signature_b64":"e79nhgpGgFWOX+ZnT9yUHOkpW/D59+r4jToPay4V/aNMLnlQZYV+2zLvp4H8u1BvS+Mw/1qi8MaJt056zJcBBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a357bb6e649704463776476303b30bee003b1d99e6f837435d2887b69164bf59","last_reissued_at":"2026-07-05T03:42:06.542863Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:42:06.542863Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"HVH: Learning a Hybrid Neural Volumetric Representation for Dynamic Hair Performance Capture","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.GR"],"primary_cat":"cs.CV","authors_text":"Christoph Lassner, Giljoo Nam, Jessica Hodgins, Michael Zollhoefer, Stephen Lombardi, Tuur Stuyck, Ziyan Wang","submitted_at":"2021-12-13T18:57:50Z","abstract_excerpt":"Capturing and rendering life-like hair is particularly challenging due to its fine geometric structure, the complex physical interaction and its non-trivial visual appearance.Yet, hair is a critical component for believable avatars. In this paper, we address the aforementioned problems: 1) we use a novel, volumetric hair representation that is com-posed of thousands of primitives. Each primitive can be rendered efficiently, yet realistically, by building on the latest advances in neural rendering. 2) To have a reliable control signal, we present a novel way of tracking hair on the strand level"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2112.06904","kind":"arxiv","version":3},"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/2112.06904/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2112.06904","created_at":"2026-07-05T03:42:06.542950+00:00"},{"alias_kind":"arxiv_version","alias_value":"2112.06904v3","created_at":"2026-07-05T03:42:06.542950+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2112.06904","created_at":"2026-07-05T03:42:06.542950+00:00"},{"alias_kind":"pith_short_12","alias_value":"UNL3W3TES4CE","created_at":"2026-07-05T03:42:06.542950+00:00"},{"alias_kind":"pith_short_16","alias_value":"UNL3W3TES4CEMN3W","created_at":"2026-07-05T03:42:06.542950+00:00"},{"alias_kind":"pith_short_8","alias_value":"UNL3W3TE","created_at":"2026-07-05T03:42:06.542950+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2607.09811","citing_title":"Detangled: A Framework for Creating, Editing, and Inferencing Feature Rich Hair Strands","ref_index":147,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/UNL3W3TES4CEMN3WI5RQHMYL5Y","json":"https://pith.science/pith/UNL3W3TES4CEMN3WI5RQHMYL5Y.json","graph_json":"https://pith.science/api/pith-number/UNL3W3TES4CEMN3WI5RQHMYL5Y/graph.json","events_json":"https://pith.science/api/pith-number/UNL3W3TES4CEMN3WI5RQHMYL5Y/events.json","paper":"https://pith.science/paper/UNL3W3TE"},"agent_actions":{"view_html":"https://pith.science/pith/UNL3W3TES4CEMN3WI5RQHMYL5Y","download_json":"https://pith.science/pith/UNL3W3TES4CEMN3WI5RQHMYL5Y.json","view_paper":"https://pith.science/paper/UNL3W3TE","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2112.06904&json=true","fetch_graph":"https://pith.science/api/pith-number/UNL3W3TES4CEMN3WI5RQHMYL5Y/graph.json","fetch_events":"https://pith.science/api/pith-number/UNL3W3TES4CEMN3WI5RQHMYL5Y/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/UNL3W3TES4CEMN3WI5RQHMYL5Y/action/timestamp_anchor","attest_storage":"https://pith.science/pith/UNL3W3TES4CEMN3WI5RQHMYL5Y/action/storage_attestation","attest_author":"https://pith.science/pith/UNL3W3TES4CEMN3WI5RQHMYL5Y/action/author_attestation","sign_citation":"https://pith.science/pith/UNL3W3TES4CEMN3WI5RQHMYL5Y/action/citation_signature","submit_replication":"https://pith.science/pith/UNL3W3TES4CEMN3WI5RQHMYL5Y/action/replication_record"}},"created_at":"2026-07-05T03:42:06.542950+00:00","updated_at":"2026-07-05T03:42:06.542950+00:00"}