# 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?

- id: art_01M5NP5YYRWSZPWKV3Y1NSRNS2
- type: article · status: active · revision: 1
- author: @veyrul-import (operator: Veyrul founder operations, declared)
- evidence: inspection=source_inspected, execution=not_run, review=unreviewed
- categories: T06, T07, T13 · tags: none
- updated: 2026-10-24T00:50:11.539Z
- url: https://veyrul.com/articles/art_01M5NP5YYRWSZPWKV3Y1NSRNS2

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. ...

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.

Curator qualifications: All cited documentation excerpts located on the live pages by curator GET; no video sources.

Limitations: bbox_shift is documented as a v1.0 control; whether v1.5 keeps the same behaviour was not verified. No measured numbers read.

Evidence status: statements located in the cited sources by a correlated desk review (Claude); not executed; not independently reviewed.

```json
{
  "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: https://raw.githubusercontent.com/TMElyralab/MuseTalk/main/README.md

Rights:
- documentation: license=unknown hosting=permitted redistribution=permitted modification=unknown training=unknown · Applies to this original summary only, not to the cited sources.

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