Facial expression synthesis has drawn much attention in the field of computer
graphics and pattern recognition. It has been widely used in face animation and
recognition. However, it is still challenging due to the high-level semantic
presence of large and non-linear face geometry variations. This paper proposes
a Geometry-Guided Generative Adversarial Network (G2-GAN) for photo-realistic
and identity-preserving facial expression synthesis. We employ facial geometry
(fiducial points) as a controllable condition to guide facial texture synthesis
with specific expression. A pair of generative adversarial subnetworks are
jointly trained towards opposite tasks: expression removal and expression
synthesis. The paired networks form a mapping cycle between neutral expression
and arbitrary expressions, which also facilitate other applications such as
face transfer and expression invariant face recognition. Experimental results
show that our method can generate compelling perceptual results on various
facial expression synthesis databases. An expression invariant face recognition
experiment is also performed to further show the advantages of our proposed
method.

本文提出一种几何引导生成对抗网络 (G2-GAN)，通过面部几何条件（标识点）来引导特定表情的面部纹理合成，实现逼真的与身份保护面部表情合成，并取得了良好的实验效果。