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.

提出一种新的使用隐式表示的解剖受限人脸模型，通过联合学习面部解剖和皮肤表面来建模，可以作为传统混合形状模型的可替代方案，并应用于形状拟合、形状编辑和性能重定向等任务。