# Compressed Skinning for Facial Blendshapes

arXiv: https://arxiv.org/abs/2406.11597 · HTML: https://mlsanity.com/paper/2406.11597/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jun 2024.

## Built on by (most cited first, 4 of 4)

- [RigAnyFace: Scaling Neural Facial Mesh Auto-Rigging with Unlabeled Data](https://mlsanity.com/paper/2511.18601/) (2025)
- [OmniFaceRig: Fully Automatic Inner-Mouth-Aware Face Rigging Across Diverse 3D Character Topologies](https://mlsanity.com/paper/2606.08043/) (2026)
- [Neural Kinematic Bases for Fluids](https://mlsanity.com/paper/2504.15657/) (2025)
- [Improving Facial Rig Semantics for Tracking and Retargeting](https://mlsanity.com/paper/2508.08429/) (2025)

## Abstract (arXiv)

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.