Physically-based simulation is a powerful approach for 3D facial animation as
the resulting deformations are governed by physical constraints, allowing to
easily resolve self-collisions, respond to external forces and perform
realistic anatomy edits. Today's methods are data-driven, where the actuations
for finite elements are inferred from captured skin geometry. Unfortunately,
these approaches have not been widely adopted due to the complexity of
initializing the material space and learning the deformation model for each
character separately, which often requires a skilled artist followed by lengthy
network training. In this work, we aim to make physics-based facial animation
more accessible by proposing a generalized physical face model that we learn
from a large 3D face dataset in a simulation-free manner. Once trained, our
model can be quickly fit to any unseen identity and produce a ready-to-animate
physical face model automatically. Fitting is as easy as providing a single 3D
face scan, or even a single face image. After fitting, we offer intuitive
animation controls, as well as the ability to retarget animations across
characters. All the while, the resulting animations allow for physical effects
like collision avoidance, gravity, paralysis, bone reshaping and more.

通过从大规模三维人脸数据集中学习而非模拟的方式，我们提出了一种通用的基于物理的人脸模型，使物理基础的人脸动画更容易使用。一旦训练完成，我们的模型可以快速适应任何未见过的身份，并自动生成一个可供动画使用的物理人脸模型。拟合过程非常简单，只需提供一个三维人脸扫描，甚至一个人脸图像。同时，我们提供直观的动画控制以及角色重定向的能力。所得到的动画能够具备碰撞避免、重力、麻痹、骨骼重塑等物理效果。