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.

翻译：基于坐标的隐式神经表示近年来迅速普及，因其在图像、几何及场景建模任务中取得了成功应用。本文提出了一种此类隐式表示的新应用场景——学习解剖约束下的人脸模型。面向特定演员的解剖约束人脸模型既是面部表现捕捉也是表现重定向领域的最新技术。尽管具有实际成功，但这些解剖模型评估速度较慢，且通常需要大量数据采集才能构建。我们提出解剖隐式人脸模型：一种隐式神经网络集成，可联合学习高保真度的面部解剖结构与皮肤表面，并可直接作为传统混合形状模型的替代方案。给定任意一组演员皮肤表面网格，以及仅包含估计颅骨与下颌骨的静态模型，我们的方法能够恢复稠密的解剖子结构，从而约束面部表面的每个点。我们通过形状拟合、形状编辑及表现重定向等多项任务验证了本方法的实用性。