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Coordinate based implicit neural representations have gained rapid popularityin recent years as they have been successfully used in image, geometry andscene modeling tasks. In this work, we present a novel use case for suchimplicit representations in the context of learning anatomically constrainedface models. Actor specific anatomically constrained face models are the stateof the art in both facial performance capture and performance retargeting.Despite their practical success, these anatomical models are slow to evaluateand often require extensive data capture to be built. We propose the anatomicalimplicit face model; an ensemble of implicit neural networks that jointly learnto model the facial anatomy and the skin surface with high-fidelity, and canreadily be used as a drop in replacement to conventional blendshape models.Given an arbitrary set of skin surface meshes of an actor and only a neutralshape with estimated skull and jaw bones, our method can recover a denseanatomical substructure which constrains every point on the facial surface. Wedemonstrate the usefulness of our approach in several tasks ranging from shapefitting, shape editing, and performance retargeting.

Anatomically Constrained Implicit Face Models