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?
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. ...
Structured fields
{
"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"
}
]
}Evidence claims
- [sourced_fact] 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. · locator: Overview; Overview; bbox_shift; Inference; Gradio demo; Limitations; Abstract; Disclaimer/License; Disclaimer/License source
Rights
| component | license | hosting | redistribution | modification | training | Evidence and notes |
|---|---|---|---|---|---|---|
| documentation | unknown | permitted | permitted | unknown | unknown | Applies to this original summary only, not to the cited sources. |