{"schema":"record/v1","id":"art_01M5NP5YYRWSZPWKV3Y1NSRNS2","type":"article","revision":1,"etag":"\"r1.s0\"","title":"How does MuseTalk define its face region and mouth-openness control, what fps does it expect, what limits does it state, and what does its MIT licence not cover?","summary":"Problem. Real-time or near-real-time dubbing of existing footage. Conditions. MuseTalk 1.0/1.5 (Tencent Music), latent-space inpainting of the lower face with a 256x256 face region, Whisper audio features. Method. (1) Use 25 fps input: the README says this matches training fps; convert lower-fps video with interpolation or ffmpeg. (2) The face-region centre matters a lot: in v1.0 use bbox_shift (positive moves the mask's upper bound toward the lower half and increases mouth openness; negative decreases it), e.g. --bbox_shift -7. (3) In the Gradio demo, generate only the first frame to tune parameters before running the full video. (4) For higher resolution, the README suggests adding a super-resolution model such as GFPGAN. Failure modes stated. Identity details such as moustache, lip shape and lip colour are not well preserved; resolution bound of the 256x256 face region. Paper abstract frames the trade-off: GAN-based real-time methods sacrifice lip-sync accuracy or dental detail. ...","status":"active","body":{"text":"Problem. Real-time or near-real-time dubbing of existing footage. Conditions. MuseTalk 1.0/1.5 (Tencent Music), latent-space inpainting of the lower face with a 256x256 face region, Whisper audio features. Method. (1) Use 25 fps input: the README says this matches training fps; convert lower-fps video with interpolation or ffmpeg. (2) The face-region centre matters a lot: in v1.0 use bbox_shift (positive moves the mask's upper bound toward the lower half and increases mouth openness; negative decreases it), e.g. --bbox_shift -7. (3) In the Gradio demo, generate only the first frame to tune parameters before running the full video. (4) For higher resolution, the README suggests adding a super-resolution model such as GFPGAN. Failure modes stated. Identity details such as moustache, lip shape and lip colour are not well preserved; resolution bound of the 256x256 face region. Paper abstract frames the trade-off: GAN-based real-time methods sacrifice lip-sync accuracy or dental detail. Rights. Code MIT with no academic/commercial limit; but test data are non-commercial research only and other models used (whisper, ft-mse-vae, dwpose, S3FD, etc.) must comply with their own licences. Limitations. v1.5 flags beyond those shown are in the README but I did not exercise them. Version/date. v1.5 released 2025-03-28; fetched 2026-10-23.\n\nCurator qualifications: All cited documentation excerpts located on the live pages by curator GET; no video sources.\n\nLimitations: bbox_shift is documented as a v1.0 control; whether v1.5 keeps the same behaviour was not verified. No measured numbers read.\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/TMElyralab/MuseTalk/main/README.md","note":"Overview; Overview; bbox_shift; Inference; Gradio demo; Limitations; Abstract; Disclaimer/License; Disclaimer/License"},{"url":"https://arxiv.org/abs/2410.10122","note":"Overview; Overview; bbox_shift; Inference; Gradio demo; Limitations; Abstract; Disclaimer/License; Disclaimer/License"}]},"categories":["T06","T07","T13"],"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/TMElyralab/MuseTalk/main/README.md","claim":"Problem. Real-time or near-real-time dubbing of existing footage. Conditions. MuseTalk 1.0/1.5 (Tencent Music), latent-space inpainting of the lower face with a 256x256 face region, Whisper audio features. Method. (1) Use 25 fps input: the README says this matches training fps; convert lower-fps video with interpolation or ffmpeg. (2) The face-region centre matters a lot: in v1.0 use bbox_shift (positive moves the mask's upper bound toward the lower half and increases mouth openness; negative decreases it), e.g. --bbox_shift -7. (3) In the Gradio demo, generate only the first frame to tune parameters before running the full video. (4) For higher resolution, the README suggests adding a super-resolution model such as GFPGAN. Failure modes stated. Identity details such as moustache, lip shape and lip colour are not well preserved; resolution bound of the 256x256 face region. Paper abstract frames the trade-off: GAN-based real-time methods sacrifice lip-sync accuracy or dental detail. Rights. Code MIT with no academic/commercial limit; but test data are non-commercial research only and other models used (whisper, ft-mse-vae, dwpose, S3FD, etc.) must comply with their own licences. Limitations. v1.5 flags beyond those shown are in the README but I did not exercise them. 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Real-time or near-real-time dubbing of existing footage. Conditions. MuseTalk 1.0/1.5 (Tencent Music), latent-space inpainting of the lower face with a 256x256 face region, Whisper audio features. Method. (1) Use 25 fps input: the README says this matches training fps; convert lower-fps video with interpolation or ffmpeg. (2) The face-region centre matters a lot: in v1.0 use bbox_shift (positive moves the mask's upper bound toward the lower half and increases mouth openness; negative decreases it), e.g. --bbox_shift -7. (3) In the Gradio demo, generate only the first frame to tune parameters before running the full video. (4) For higher resolution, the README suggests adding a super-resolution model such as GFPGAN. Failure modes stated. Identity details such as moustache, lip shape and lip colour are not well preserved; resolution bound of the 256x256 face region. Paper abstract frames the trade-off: GAN-based real-time methods sacrifice lip-sync accuracy or dental detail. Rights. Code MIT with no academic/commercial limit; but test data are non-commercial research only and other models used (whisper, ft-mse-vae, dwpose, S3FD, etc.) must comply with their own licences. Limitations. v1.5 flags beyond those shown are in the README but I did not exercise them. Version/date. v1.5 released 2025-03-28; fetched 2026-10-23.\n\nCurator qualifications: All cited documentation excerpts located on the live pages by curator GET; no video sources.\n\nLimitations: bbox_shift is documented as a v1.0 control; whether v1.5 keeps the same behaviour was not verified. No measured numbers read.\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/TMElyralab/MuseTalk/main/README.md","note":"Overview; Overview; bbox_shift; Inference; Gradio demo; Limitations; Abstract; Disclaimer/License; Disclaimer/License"},{"url":"https://arxiv.org/abs/2410.10122","note":"Overview; Overview; bbox_shift; Inference; Gradio demo; Limitations; Abstract; Disclaimer/License; Disclaimer/License"}]},"tags":[],"type":"article","links":[],"title":"How does MuseTalk define its face region and mouth-openness control, what fps does it expect, what limits does it state, and what does its MIT licence not cover?","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. Real-time or near-real-time dubbing of existing footage. Conditions. MuseTalk 1.0/1.5 (Tencent Music), latent-space inpainting of the lower face with a 256x256 face region, Whisper audio features. Method. (1) Use 25 fps input: the README says this matches training fps; convert lower-fps video with interpolation or ffmpeg. (2) The face-region centre matters a lot: in v1.0 use bbox_shift (positive moves the mask's upper bound toward the lower half and increases mouth openness; negative decreases it), e.g. --bbox_shift -7. (3) In the Gradio demo, generate only the first frame to tune parameters before running the full video. (4) For higher resolution, the README suggests adding a super-resolution model such as GFPGAN. Failure modes stated. Identity details such as moustache, lip shape and lip colour are not well preserved; resolution bound of the 256x256 face region. 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