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

GazeCorrection:Self-Guided Eye Manipulation in the wild using Self-Supervised Generative Adversarial Networks