{"paper":{"title":"MMTalker: Multiresolution 3D Talking Head Synthesis with Multimodal Feature Fusion","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"MMTalker synthesizes detailed 3D talking heads from speech by combining UV mesh parameterization with dual cross-attention fusion of audio and geometric features.","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Bin Liu, Bo Li, Zhifen He, Zhixiang Xiong","submitted_at":"2026-04-03T10:17:39Z","abstract_excerpt":"Speech-driven three-dimensional (3D) facial animation synthesis aims to build a mapping from one-dimensional (1D) speech signals to time-varying 3D facial motion signals. Current methods still face challenges in maintaining lip-sync accuracy and producing realistic facial expressions, primarily due to the highly ill-posed nature of this cross-modal mapping. In this paper, we introduce a novel 3D audio-driven facial animation synthesis method through multi-resolution representation and multi-modal feature fusion, called MMTalker which can accurately reconstruct the rich details of 3D facial mot"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"MMTalker can accurately reconstruct the rich details of 3D facial motion and achieves significant improvements over state-of-the-art methods, especially in the synchronization accuracy of lip and eye movements.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That the combination of non-uniform differentiable sampling on UV-parameterized meshes and dual cross-attention fusion will resolve the ill-posed speech-to-3D-motion mapping without introducing artifacts or requiring extensive post-processing.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"MMTalker combines multi-resolution mesh sampling with residual graph convolutions and dual cross-attention to synthesize accurate 3D talking head motions from audio.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"MMTalker synthesizes detailed 3D talking heads from speech by combining UV mesh parameterization with dual cross-attention fusion of audio and geometric features.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"e09bc56e81fcaa308190a6a89eda3f0593c4cbce5554b00468c6d6c22b1cb547"},"source":{"id":"2604.02941","kind":"arxiv","version":2},"verdict":{"id":"c5d10b79-6e4a-48c7-914d-712d63bd7642","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-13T19:43:00.793617Z","strongest_claim":"MMTalker can accurately reconstruct the rich details of 3D facial motion and achieves significant improvements over state-of-the-art methods, especially in the synchronization accuracy of lip and eye movements.","one_line_summary":"MMTalker combines multi-resolution mesh sampling with residual graph convolutions and dual cross-attention to synthesize accurate 3D talking head motions from audio.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That the combination of non-uniform differentiable sampling on UV-parameterized meshes and dual cross-attention fusion will resolve the ill-posed speech-to-3D-motion mapping without introducing artifacts or requiring extensive post-processing.","pith_extraction_headline":"MMTalker synthesizes detailed 3D talking heads from speech by combining UV mesh parameterization with dual cross-attention fusion of audio and geometric features."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2604.02941/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":1,"snapshot_sha256":"6eb294c43f91bdebe296fcf460f86187f20249dfa51e514bb98296680b13ae4d"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}