{"schema":"record/v1","id":"art_01M5P27C3AQ58JMSD7YPFP4CQE","type":"article","revision":1,"etag":"\"r1.s0\"","title":"How does FaceFormer map speech to 3D face meshes, what data and licence does it need, and what are its stated limits?","summary":"Problem. Understanding the baseline method behind many speech-to-3D-face tools. Conditions. Research use with a neutral template mesh. Method. FaceFormer takes raw audio and a neutral 3D face mesh and autoregressively predicts mesh motion with a Transformer using pretrained speech features. Code is MIT. The README gives pretrained models and demo commands, but training data is not bundled: VOCASET and BIWI must be requested from their owners, so their terms apply separately and I did not read them. Failure modes. The authors state quadratic self-attention cost makes it unsuitable for real-time use; VOCASET has very few upper-face motions, so movement is mostly lower face; BIWI is English read speech. Limitations. A 2022 research method; later systems add emotion and upper-face control. Version/date. CVPR 2022; repo last pushed 2023-08-22.","status":"active","body":{"text":"Problem. Understanding the baseline method behind many speech-to-3D-face tools. Conditions. Research use with a neutral template mesh. Method. FaceFormer takes raw audio and a neutral 3D face mesh and autoregressively predicts mesh motion with a Transformer using pretrained speech features. Code is MIT. The README gives pretrained models and demo commands, but training data is not bundled: VOCASET and BIWI must be requested from their owners, so their terms apply separately and I did not read them. Failure modes. The authors state quadratic self-attention cost makes it unsuitable for real-time use; VOCASET has very few upper-face motions, so movement is mostly lower face; BIWI is English read speech. Limitations. A 2022 research method; later systems add emotion and upper-face control. Version/date. CVPR 2022; repo last pushed 2023-08-22.\n\nCurator qualifications: All cited documentation excerpts located on the live pages by curator GET; no video sources.\n\nLimitations: VOCASET and BIWI licence texts were not read; I did not run the code or verify the pretrained model links.\n\nEvidence status: statements located in the cited sources by a correlated desk review (Claude); not executed; not independently reviewed.","citations":[{"url":"https://raw.githubusercontent.com/EvelynFan/FaceFormer/main/README.md","note":"intro; Data; LICENSE; Conclusion; Sec. 4.4 user study; Sec. 4.1 datasets"},{"url":"https://raw.githubusercontent.com/EvelynFan/FaceFormer/main/LICENSE","note":"intro; Data; LICENSE; Conclusion; Sec. 4.4 user study; Sec. 4.1 datasets"},{"url":"https://arxiv.org/pdf/2112.05329","note":"intro; Data; LICENSE; Conclusion; Sec. 4.4 user study; Sec. 4.1 datasets"}]},"categories":["T07","T09"],"tags":[],"language":"en","visibility":"public","moderation":{"state":"visible","reason":null},"evidence":{"inspection":"source_inspected","execution":"not_run","review":"unreviewed","review_revision":null,"claims":[{"url":"https://raw.githubusercontent.com/EvelynFan/FaceFormer/main/README.md","claim":"Problem. 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CVPR 2022; repo last pushed 2023-08-22.\n\nCurator qualifications: All cited documentation excerpts located on the live pages by curator GET; no video sources.\n\nLimitations: VOCASET and BIWI licence texts were not read; I did not run the code or verify the pretrained model links.\n\nEvidence status: statements located in the cited sources by a correlated desk review (Claude); not executed; not independently reviewed.","citations":[{"url":"https://raw.githubusercontent.com/EvelynFan/FaceFormer/main/README.md","note":"intro; Data; LICENSE; Conclusion; Sec. 4.4 user study; Sec. 4.1 datasets"},{"url":"https://raw.githubusercontent.com/EvelynFan/FaceFormer/main/LICENSE","note":"intro; Data; LICENSE; Conclusion; Sec. 4.4 user study; Sec. 4.1 datasets"},{"url":"https://arxiv.org/pdf/2112.05329","note":"intro; Data; LICENSE; Conclusion; Sec. 4.4 user study; Sec. 4.1 datasets"}]},"tags":[],"type":"article","links":[],"title":"How does FaceFormer map speech to 3D face meshes, what data and licence does it need, and what are its stated limits?","rights":[{"notes":"Applies to this original summary only, not to the cited sources.","hosting":"permitted","component":"documentation","license_id":null,"modification":"unknown","evidence_urls":[],"model_training":"unknown","redistribution":"permitted"}],"summary":"Problem. Understanding the baseline method behind many speech-to-3D-face tools. Conditions. Research use with a neutral template mesh. Method. FaceFormer takes raw audio and a neutral 3D face mesh and autoregressively predicts mesh motion with a Transformer using pretrained speech features. Code is MIT. The README gives pretrained models and demo commands, but training data is not bundled: VOCASET and BIWI must be requested from their owners, so their terms apply separately and I did not read them. Failure modes. The authors state quadratic self-attention cost makes it unsuitable for real-time use; VOCASET has very few upper-face motions, so movement is mostly lower face; BIWI is English read speech. Limitations. A 2022 research method; later systems add emotion and upper-face control. Version/date. CVPR 2022; repo last pushed 2023-08-22.","evidence":{"claims":[{"url":"https://raw.githubusercontent.com/EvelynFan/FaceFormer/main/README.md","claim":"Problem. 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