Anatomically Constrained Implicit Face Models

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.

Link to publication page: https://studios.disneyresearch.com/2024/06/03/anatomically-constrained-implicit-face-models/