{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:76SELWYDWF2O3ITJR2KWOA6TI7","short_pith_number":"pith:76SELWYD","schema_version":"1.0","canonical_sha256":"ffa445db03b174eda2698e956703d347c27326faacc1d07a0730e0eb50acc90d","source":{"kind":"arxiv","id":"2402.06149","version":2},"attestation_state":"computed","paper":{"title":"HeadStudio: Text to Animatable Head Avatars with 3D Gaussian Splatting","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Fan Ma, Hehe Fan, Yi Yang, Zhenglin Zhou, Zongxin Yang","submitted_at":"2024-02-09T02:58:37Z","abstract_excerpt":"Creating digital avatars from textual prompts has long been a desirable yet challenging task. Despite the promising results achieved with 2D diffusion priors, current methods struggle to create high-quality and consistent animated avatars efficiently. Previous animatable head models like FLAME have difficulty in accurately representing detailed texture and geometry. Additionally, high-quality 3D static representations face challenges in semantically driving with dynamic priors. In this paper, we introduce \\textbf{HeadStudio}, a novel framework that utilizes 3D Gaussian splatting to generate re"},"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":"2402.06149","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-02-09T02:58:37Z","cross_cats_sorted":[],"title_canon_sha256":"a8d48a86e7476d36171c83a9660d8821bb60e07f8af99079f10cc5cdcde9807e","abstract_canon_sha256":"20d78f2167895d50b7de60f5e935a0657cf06b05fdd9d648efbe3392a098c591"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:52:37.501298Z","signature_b64":"XbnUaAnBgZN2p/zT1Wumse8AkBJDif9rs1xcmU1rGEQlfkHhCPF4dY4Dbgr0lXw9Nfxgu6OSwNdZx99Xkoe0CA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ffa445db03b174eda2698e956703d347c27326faacc1d07a0730e0eb50acc90d","last_reissued_at":"2026-07-05T09:52:37.500885Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:52:37.500885Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"HeadStudio: Text to Animatable Head Avatars with 3D Gaussian Splatting","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Fan Ma, Hehe Fan, Yi Yang, Zhenglin Zhou, Zongxin Yang","submitted_at":"2024-02-09T02:58:37Z","abstract_excerpt":"Creating digital avatars from textual prompts has long been a desirable yet challenging task. Despite the promising results achieved with 2D diffusion priors, current methods struggle to create high-quality and consistent animated avatars efficiently. Previous animatable head models like FLAME have difficulty in accurately representing detailed texture and geometry. Additionally, high-quality 3D static representations face challenges in semantically driving with dynamic priors. In this paper, we introduce \\textbf{HeadStudio}, a novel framework that utilizes 3D Gaussian splatting to generate re"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2402.06149","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/2402.06149/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":"2402.06149","created_at":"2026-07-05T09:52:37.500940+00:00"},{"alias_kind":"arxiv_version","alias_value":"2402.06149v2","created_at":"2026-07-05T09:52:37.500940+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2402.06149","created_at":"2026-07-05T09:52:37.500940+00:00"},{"alias_kind":"pith_short_12","alias_value":"76SELWYDWF2O","created_at":"2026-07-05T09:52:37.500940+00:00"},{"alias_kind":"pith_short_16","alias_value":"76SELWYDWF2O3ITJ","created_at":"2026-07-05T09:52:37.500940+00:00"},{"alias_kind":"pith_short_8","alias_value":"76SELWYD","created_at":"2026-07-05T09:52:37.500940+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2509.02357","citing_title":"Category-Aware 3D Object Composition with Disentangled Texture and Shape Multi-view Diffusion","ref_index":70,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/76SELWYDWF2O3ITJR2KWOA6TI7","json":"https://pith.science/pith/76SELWYDWF2O3ITJR2KWOA6TI7.json","graph_json":"https://pith.science/api/pith-number/76SELWYDWF2O3ITJR2KWOA6TI7/graph.json","events_json":"https://pith.science/api/pith-number/76SELWYDWF2O3ITJR2KWOA6TI7/events.json","paper":"https://pith.science/paper/76SELWYD"},"agent_actions":{"view_html":"https://pith.science/pith/76SELWYDWF2O3ITJR2KWOA6TI7","download_json":"https://pith.science/pith/76SELWYDWF2O3ITJR2KWOA6TI7.json","view_paper":"https://pith.science/paper/76SELWYD","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2402.06149&json=true","fetch_graph":"https://pith.science/api/pith-number/76SELWYDWF2O3ITJR2KWOA6TI7/graph.json","fetch_events":"https://pith.science/api/pith-number/76SELWYDWF2O3ITJR2KWOA6TI7/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/76SELWYDWF2O3ITJR2KWOA6TI7/action/timestamp_anchor","attest_storage":"https://pith.science/pith/76SELWYDWF2O3ITJR2KWOA6TI7/action/storage_attestation","attest_author":"https://pith.science/pith/76SELWYDWF2O3ITJR2KWOA6TI7/action/author_attestation","sign_citation":"https://pith.science/pith/76SELWYDWF2O3ITJR2KWOA6TI7/action/citation_signature","submit_replication":"https://pith.science/pith/76SELWYDWF2O3ITJR2KWOA6TI7/action/replication_record"}},"created_at":"2026-07-05T09:52:37.500940+00:00","updated_at":"2026-07-05T09:52:37.500940+00:00"}