Anatomically Constrained Implicit Face Models
Prashanth Chandran Gaspard Zoss
Coordinate based implicit neural representations have gained rapid popularity
in recent years as they have been successfully used in image, geometry and
scene modeling tasks. In this work, we present a novel use case for such
implicit representations in the context of learning anatomically constrained
face models. Actor specific anatomically constrained face models are the state
of the art in both facial performance capture and performance retargeting.
Despite their practical success, these anatomical models are slow to evaluate
and often require extensive data capture to be built. We propose the anatomical
implicit face model; an ensemble of implicit neural networks that jointly learn
to model the facial anatomy and the skin surface with high-fidelity, and can
readily 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 neutral
shape with estimated skull and jaw bones, our method can recover a dense
anatomical substructure which constrains every point on the facial surface. We
demonstrate the usefulness of our approach in several tasks ranging from shape
fitting, shape editing, and performance retargeting.