  3D facial animation is often produced by manipulating facial deformation
models (or rigs), that are traditionally parameterized by expression controls.
A key component that is usually overlooked is expression 'style', as in, how a
particular expression is performed. Although it is common to define a semantic
basis of expressions that characters can perform, most characters perform each
expression in their own style. To date, style is usually entangled with the
expression, and it is not possible to transfer the style of one character to
another when considering facial animation. We present a new face model, based
on a data-driven implicit neural physics model, that can be driven by both
expression and style separately. At the core, we present a framework for
learning implicit physics-based actuations for multiple subjects
simultaneously, trained on a few arbitrary performance capture sequences from a
small set of identities. Once trained, our method allows generalized
physics-based facial animation for any of the trained identities, extending to
unseen performances. Furthermore, it grants control over the animation style,
enabling style transfer from one character to another or blending styles of
different characters. Lastly, as a physics-based model, it is capable of
synthesizing physical effects, such as collision handling, setting our method
apart from conventional approaches.
