  Gaze correction aims to redirect the person's gaze into the camera by
manipulating the eye region, and it can be considered as a specific image
resynthesis problem. Gaze correction has a wide range of applications in real
life, such as taking a picture with staring at the camera. In this paper, we
propose a novel method that is based on the inpainting model to learn from the
face image to fill in the missing eye regions with new contents representing
corrected eye gaze. Moreover, our model does not require the training dataset
labeled with the specific head pose and eye angle information, thus, the
training data is easy to collect. To retain the identity information of the eye
region in the original input, we propose a self-guided pretrained model to
learn the angle-invariance feature. Experiments show our model achieves very
compelling gaze-corrected results in the wild dataset which is collected from
the website and will be introduced in details. Code is available at
https://github.com/zhangqianhui/GazeCorrection.
