{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:5HPNBIGXEQO62ATA72MIUILSE7","short_pith_number":"pith:5HPNBIGX","schema_version":"1.0","canonical_sha256":"e9ded0a0d7241ded0260fe988a217227e651f509404dbf06bf919aab497416a8","source":{"kind":"arxiv","id":"2407.15070","version":2},"attestation_state":"computed","paper":{"title":"GPHM: Gaussian Parametric Head Model for Monocular Head Avatar Reconstruction","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Qingyao Wu, Yebin Liu, Yuelang Xu, Zhaoqi Su","submitted_at":"2024-07-21T06:03:11Z","abstract_excerpt":"Creating high-fidelity 3D human head avatars is crucial for applications in VR/AR, digital human, and film production. Recent advances have leveraged morphable face models to generate animated head avatars from easily accessible data, representing varying identities and expressions within a low-dimensional parametric space. However, existing methods often struggle with modeling complex appearance details, e.g., hairstyles, and suffer from low rendering quality and efficiency. In this paper we introduce a novel approach, 3D Gaussian Parametric Head Model, which employs 3D Gaussians to accuratel"},"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":"2407.15070","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2024-07-21T06:03:11Z","cross_cats_sorted":[],"title_canon_sha256":"1028413c9eca4772e551df2697fddf4a80b44839d20e47bad2ceeed4d5f0d05b","abstract_canon_sha256":"f130e4f1999ec322533a942ac6fd59e5cddba073f83f9bd1e5fa19614871b286"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:24:17.429565Z","signature_b64":"bET9KLt8niQO0Ss28++jr0TPgvdZHhL1FUbcSwsHcsvecYvAFa5VkGPBtJ7wktbwRRxCoiK6rdkQfJqsV9NlAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e9ded0a0d7241ded0260fe988a217227e651f509404dbf06bf919aab497416a8","last_reissued_at":"2026-07-05T09:24:17.429014Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:24:17.429014Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"GPHM: Gaussian Parametric Head Model for Monocular Head Avatar Reconstruction","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Qingyao Wu, Yebin Liu, Yuelang Xu, Zhaoqi Su","submitted_at":"2024-07-21T06:03:11Z","abstract_excerpt":"Creating high-fidelity 3D human head avatars is crucial for applications in VR/AR, digital human, and film production. Recent advances have leveraged morphable face models to generate animated head avatars from easily accessible data, representing varying identities and expressions within a low-dimensional parametric space. However, existing methods often struggle with modeling complex appearance details, e.g., hairstyles, and suffer from low rendering quality and efficiency. In this paper we introduce a novel approach, 3D Gaussian Parametric Head Model, which employs 3D Gaussians to accuratel"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2407.15070","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/2407.15070/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":"2407.15070","created_at":"2026-07-05T09:24:17.429073+00:00"},{"alias_kind":"arxiv_version","alias_value":"2407.15070v2","created_at":"2026-07-05T09:24:17.429073+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2407.15070","created_at":"2026-07-05T09:24:17.429073+00:00"},{"alias_kind":"pith_short_12","alias_value":"5HPNBIGXEQO6","created_at":"2026-07-05T09:24:17.429073+00:00"},{"alias_kind":"pith_short_16","alias_value":"5HPNBIGXEQO62ATA","created_at":"2026-07-05T09:24:17.429073+00:00"},{"alias_kind":"pith_short_8","alias_value":"5HPNBIGX","created_at":"2026-07-05T09:24:17.429073+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.05912","citing_title":"Self-Learning Expression Deformations for Data-Efficient Gaussian Avatars","ref_index":74,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/5HPNBIGXEQO62ATA72MIUILSE7","json":"https://pith.science/pith/5HPNBIGXEQO62ATA72MIUILSE7.json","graph_json":"https://pith.science/api/pith-number/5HPNBIGXEQO62ATA72MIUILSE7/graph.json","events_json":"https://pith.science/api/pith-number/5HPNBIGXEQO62ATA72MIUILSE7/events.json","paper":"https://pith.science/paper/5HPNBIGX"},"agent_actions":{"view_html":"https://pith.science/pith/5HPNBIGXEQO62ATA72MIUILSE7","download_json":"https://pith.science/pith/5HPNBIGXEQO62ATA72MIUILSE7.json","view_paper":"https://pith.science/paper/5HPNBIGX","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2407.15070&json=true","fetch_graph":"https://pith.science/api/pith-number/5HPNBIGXEQO62ATA72MIUILSE7/graph.json","fetch_events":"https://pith.science/api/pith-number/5HPNBIGXEQO62ATA72MIUILSE7/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/5HPNBIGXEQO62ATA72MIUILSE7/action/timestamp_anchor","attest_storage":"https://pith.science/pith/5HPNBIGXEQO62ATA72MIUILSE7/action/storage_attestation","attest_author":"https://pith.science/pith/5HPNBIGXEQO62ATA72MIUILSE7/action/author_attestation","sign_citation":"https://pith.science/pith/5HPNBIGXEQO62ATA72MIUILSE7/action/citation_signature","submit_replication":"https://pith.science/pith/5HPNBIGXEQO62ATA72MIUILSE7/action/replication_record"}},"created_at":"2026-07-05T09:24:17.429073+00:00","updated_at":"2026-07-05T09:24:17.429073+00:00"}