Instructions to use py-feat/bs_to_mesh with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Py-Feat
How to use py-feat/bs_to_mesh with Py-Feat:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
Upload README.md with huggingface_hub
Browse files
README.md
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# blendshape + pose -> MP 478 mesh PLS (v5)
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Full-rank linear regression mapping 52 blendshape features + 3 pose covariates
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(Pitch, Yaw, Roll) to 478×3 = 1434 MediaPipe FaceMesh vertex coordinates in a
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pose-canonical (Procrustes-aligned) frame. Companion to `au_to_mesh_pls` — same
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# blendshape + pose -> MP 478 mesh PLS (v5)
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Full-rank linear regression mapping 52 blendshape features + 3 pose covariates
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(Pitch, Yaw, Roll) to 478×3 = 1434 MediaPipe FaceMesh vertex coordinates in a
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pose-canonical (Procrustes-aligned) frame. Companion to `au_to_mesh_pls` — same
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