  Muscle-based systems have the potential to provide both anatomical accuracy
and semantic interpretability as compared to blendshape models; however, a lack
of expressivity and differentiability has limited their impact. Thus, we
propose modifying a recently developed rather expressive muscle-based system in
order to make it fully-differentiable; in fact, our proposed modifications
allow this physically robust and anatomically accurate muscle model to
conveniently be driven by an underlying blendshape basis. Our formulation is
intuitive, natural, as well as monolithically and fully coupled such that one
can differentiate the model from end to end, which makes it viable for both
optimization and learning-based approaches for a variety of applications. We
illustrate this with a number of examples including both shape matching of
three-dimensional geometry as as well as the automatic determination of a
three-dimensional facial pose from a single two-dimensional RGB image without
using markers or depth information.
