While current face animation methods can manipulate expressions individually,
they suffer from several limitations. The expressions manipulated by some
motion-based facial reenactment models are crude. Other ideas modeled with
facial action units cannot generalize to arbitrary expressions not covered by
annotations. In this paper, we introduce a novel Geometry-aware Facial
Expression Translation (GaFET) framework, which is based on parametric 3D
facial representations and can stably decoupled expression. Among them, a
Multi-level Feature Aligned Transformer is proposed to complement non-geometric
facial detail features while addressing the alignment challenge of spatial
features. Further, we design a De-expression model based on StyleGAN, in order
to reduce the learning difficulty of GaFET in unpaired "in-the-wild" images.
Extensive qualitative and quantitative experiments demonstrate that we achieve
higher-quality and more accurate facial expression transfer results compared to
state-of-the-art methods, and demonstrate applicability of various poses and
complex textures. Besides, videos or annotated training data are omitted,
making our method easier to use and generalize.

基于参数化的三维面部表情表示，我们提出了一种新颖的面部表情转化框架（GaFET），该框架可以稳定地分解表情，并通过多级特征对齐变换器和基于 StyleGAN 的去表情模型解决了非几何面部细节特征和空间特征对齐的问题，进而实现了与当前最先进方法相比更高质量和更准确的面部表情转换结果，并且适用于各种姿势和复杂纹理。此外，我们的方法省略了视频或注释训练数据，使得使用和推广更加简单。

GaFET：学习自野外图像的几何感知面部表情翻译

GaFET: Learning Geometry-aware Facial Expression Translation from  In-The-Wild Images

Realistic face rendering from multi-view images is beneficial to various
computer vision and graphics applications. Due to the complex spatially-varying
reflectance properties and geometry characteristics of faces, however, it
remains challenging to recover 3D facial representations both faithfully and
efficiently in the current studies. This paper presents a novel 3D face
rendering model, namely NeuFace, to learn accurate and physically-meaningful
underlying 3D representations by neural rendering techniques. It naturally
incorporates the neural BRDFs into physically based rendering, capturing
sophisticated facial geometry and appearance clues in a collaborative manner.
Specifically, we introduce an approximated BRDF integration and a simple yet
new low-rank prior, which effectively lower the ambiguities and boost the
performance of the facial BRDFs. Extensive experiments demonstrate the
superiority of NeuFace in human face rendering, along with a decent
generalization ability to common objects.

论文提出了一种名为 NeuFace 的新型 3D 面部渲染模型，可以通过神经渲染技术学习准确和具有物理意义的底层 3D 表示。