Compressed Skinning for Facial Blendshapes
<|reference_start|>Compressed Skinning for Facial Blendshapes: We present a new method to bake classical facial animation blendshapes into a fast linear blend skinning representation. Previous work explored skinning decomposition methods that approximate general animated meshes using a dense set of bone transformations; these optimizers typically alternate between optimizing for the bone transformations and the skinning weights.We depart from this alternating scheme and propose a new approach based on proximal algorithms, which effectively means adding a projection step to the popular Adam optimizer. This approach is very flexible and allows us to quickly experiment with various additional constraints and/or loss functions. Specifically, we depart from the classical skinning paradigms and restrict the transformation coefficients to contain only about 10% non-zeros, while achieving similar accuracy and visual quality as the state-of-the-art. The sparse storage enables our method to deliver significant savings in terms of both memory and run-time speed. We include a compact implementation of our new skinning decomposition method in PyTorch, which is easy to experiment with and modify to related problems.<|reference_end|>
@article{kavan2024compressed,
    title={Compressed Skinning for Facial Blendshapes},
    author={Ladislav Kavan, John Doublestein, Martin Prazak, Matthew Cioffi, Doug
  Roble},
    journal={arXiv preprint arXiv:2406.11597},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.11597},
    primaryClass={cs.GR}
}