Facial Landmarks and Expression Label Guided Photorealistic Facial Expression Synthesis

Facial expression manipulation plays an increasingly important role in the field of computer graphics and has been widely used in generating facial animations. However, it is still a very challenging task as it needs full understanding of the input face and very depending on the facial appearance. In this paper, we present an end-to-end generative adversarial network for facial expression synthesis. Given the facial landmarks and the expression label of a target image, our method automatically generates a corresponding expression facial image with the identity information and facial details well preserved. Both qualitative and quantitative experiments are conducted on the CK+ and Oulu-CASIA datasets. Experimental results show that our method has the compelling perceptual results even there exist large differences in facial shapes for unseen subjects.