We propose a simple yet powerful Landmark guided Generative Adversarial
Network (LandmarkGAN) for the facial expression-to-expression translation using
a single image, which is an important and challenging task in computer vision
since the expression-to-expression translation is a non-linear and non-aligned
problem. Moreover, it requires a high-level semantic understanding between the
input and output images since the objects in images can have arbitrary poses,
sizes, locations, backgrounds, and self-occlusions. To tackle this problem, we
propose utilizing facial landmark information explicitly. Since it is a
challenging problem, we split it into two sub-tasks, (i) category-guided
landmark generation, and (ii) landmark-guided expression-to-expression
translation. Two sub-tasks are trained in an end-to-end fashion that aims to
enjoy the mutually improved benefits from the generated landmarks and
expressions. Compared with current keypoint-guided approaches, the proposed
LandmarkGAN only needs a single facial image to generate various expressions.
Extensive experimental results on four public datasets demonstrate that the
proposed LandmarkGAN achieves better results compared with state-of-the-art
approaches only using a single image. The code is available at
this https URL.

本文提出利用面部标志信息的地标引导生成对抗网络（LandmarkGAN）, 用于使用单张图像进行面部表情转换，实现了在同样只有一张图像的情况下，比现有的基于关键点指导法更好地实现了面部表情之间的转换。