{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:HYN5362ZNZ3MBYAKAE22ZA2LLB","short_pith_number":"pith:HYN5362Z","schema_version":"1.0","canonical_sha256":"3e1bddfb596e76c0e00a0135ac834b586aabab780b52f0560d989f4a4a2f805b","source":{"kind":"arxiv","id":"2506.14742","version":1},"attestation_state":"computed","paper":{"title":"SyncTalk++: High-Fidelity and Efficient Synchronized Talking Heads Synthesis Using Gaussian Splatting","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Hao Zhao, Hongyan Liu, Hui Tian, Jun He, Junyuan Ma, Wentao Hu, Xiangyu Zhu, Xiaomei Zhang, Zhaoxin Fan, Ziqiao Peng","submitted_at":"2025-06-17T17:22:12Z","abstract_excerpt":"Achieving high synchronization in the synthesis of realistic, speech-driven talking head videos presents a significant challenge. A lifelike talking head requires synchronized coordination of subject identity, lip movements, facial expressions, and head poses. The absence of these synchronizations is a fundamental flaw, leading to unrealistic results. To address the critical issue of synchronization, identified as the ''devil'' in creating realistic talking heads, we introduce SyncTalk++, which features a Dynamic Portrait Renderer with Gaussian Splatting to ensure consistent subject identity p"},"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":"2506.14742","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-06-17T17:22:12Z","cross_cats_sorted":[],"title_canon_sha256":"652be8f1b239bb3a0eabea0d232b19c6aa4e1c548c6090fc818ba3b8887134ca","abstract_canon_sha256":"b461a37bf8dd1be66361afd912ba5a49b723cefd5c9bfcad667ee8dd85ccf8a7"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:23:12.143346Z","signature_b64":"9+0rafHEgrz+JSKatUaTWwZsaoBL+uTqigSMNTUYqc7z1iL+kHBcL5Imi3DapQH3D+A0weyKYiw9sSM4IzpbBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3e1bddfb596e76c0e00a0135ac834b586aabab780b52f0560d989f4a4a2f805b","last_reissued_at":"2026-07-05T11:23:12.142548Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:23:12.142548Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"SyncTalk++: High-Fidelity and Efficient Synchronized Talking Heads Synthesis Using Gaussian Splatting","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Hao Zhao, Hongyan Liu, Hui Tian, Jun He, Junyuan Ma, Wentao Hu, Xiangyu Zhu, Xiaomei Zhang, Zhaoxin Fan, Ziqiao Peng","submitted_at":"2025-06-17T17:22:12Z","abstract_excerpt":"Achieving high synchronization in the synthesis of realistic, speech-driven talking head videos presents a significant challenge. A lifelike talking head requires synchronized coordination of subject identity, lip movements, facial expressions, and head poses. The absence of these synchronizations is a fundamental flaw, leading to unrealistic results. To address the critical issue of synchronization, identified as the ''devil'' in creating realistic talking heads, we introduce SyncTalk++, which features a Dynamic Portrait Renderer with Gaussian Splatting to ensure consistent subject identity p"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.14742","kind":"arxiv","version":1},"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/2506.14742/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":"2506.14742","created_at":"2026-07-05T11:23:12.142652+00:00"},{"alias_kind":"arxiv_version","alias_value":"2506.14742v1","created_at":"2026-07-05T11:23:12.142652+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.14742","created_at":"2026-07-05T11:23:12.142652+00:00"},{"alias_kind":"pith_short_12","alias_value":"HYN5362ZNZ3M","created_at":"2026-07-05T11:23:12.142652+00:00"},{"alias_kind":"pith_short_16","alias_value":"HYN5362ZNZ3MBYAK","created_at":"2026-07-05T11:23:12.142652+00:00"},{"alias_kind":"pith_short_8","alias_value":"HYN5362Z","created_at":"2026-07-05T11:23:12.142652+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.30019","citing_title":"OmniDance: Multimodal Driven Dance Video Generation with Large-scale Internet Data","ref_index":28,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/HYN5362ZNZ3MBYAKAE22ZA2LLB","json":"https://pith.science/pith/HYN5362ZNZ3MBYAKAE22ZA2LLB.json","graph_json":"https://pith.science/api/pith-number/HYN5362ZNZ3MBYAKAE22ZA2LLB/graph.json","events_json":"https://pith.science/api/pith-number/HYN5362ZNZ3MBYAKAE22ZA2LLB/events.json","paper":"https://pith.science/paper/HYN5362Z"},"agent_actions":{"view_html":"https://pith.science/pith/HYN5362ZNZ3MBYAKAE22ZA2LLB","download_json":"https://pith.science/pith/HYN5362ZNZ3MBYAKAE22ZA2LLB.json","view_paper":"https://pith.science/paper/HYN5362Z","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2506.14742&json=true","fetch_graph":"https://pith.science/api/pith-number/HYN5362ZNZ3MBYAKAE22ZA2LLB/graph.json","fetch_events":"https://pith.science/api/pith-number/HYN5362ZNZ3MBYAKAE22ZA2LLB/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/HYN5362ZNZ3MBYAKAE22ZA2LLB/action/timestamp_anchor","attest_storage":"https://pith.science/pith/HYN5362ZNZ3MBYAKAE22ZA2LLB/action/storage_attestation","attest_author":"https://pith.science/pith/HYN5362ZNZ3MBYAKAE22ZA2LLB/action/author_attestation","sign_citation":"https://pith.science/pith/HYN5362ZNZ3MBYAKAE22ZA2LLB/action/citation_signature","submit_replication":"https://pith.science/pith/HYN5362ZNZ3MBYAKAE22ZA2LLB/action/replication_record"}},"created_at":"2026-07-05T11:23:12.142652+00:00","updated_at":"2026-07-05T11:23:12.142652+00:00"}