The creation of high-fidelity computer-generated (CG) characters used in film
and gaming requires intensive manual labor and a comprehensive set of facial
assets to be captured with complex hardware, resulting in high cost and long
production cycles. In order to simplify and accelerate this digitization
process, we propose a framework for the automatic generation of high-quality
dynamic facial assets, including rigs which can be readily deployed for artists
to polish. Our framework takes a single scan as input to generate a set of
personalized blendshapes, dynamic and physically-based textures, as well as
secondary facial components (e.g., teeth and eyeballs). Built upon a facial
database consisting of pore-level details, with over $4,000$ scans of varying
expressions and identities, we adopt a self-supervised neural network to learn
personalized blendshapes from a set of template expressions. We also model the
joint distribution between identities and expressions, enabling the inference
of the full set of personalized blendshapes with dynamic appearances from a
single neutral input scan. Our generated personalized face rig assets are
seamlessly compatible with cutting-edge industry pipelines for facial animation
and rendering. We demonstrate that our framework is robust and effective by
inferring on a wide range of novel subjects, and illustrate compelling
rendering results while animating faces with generated customized
physically-based dynamic textures.

通过使用自监督神经网络，本文提出了一个自动生成高质量动态面部组件的框架，包括可以快速部署供艺术家打磨的模型，并展示了在广泛的新主题上推断的鲁棒性和有效性，同时通过生成定制的基于物理的动态纹理来给角色面部制作更生动的动画。