  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.
