  Recent advances in deep generative models have demonstrated impressive
results in photo-realistic facial image synthesis and editing. Facial
expressions are inherently the result of muscle movement. However, existing
neural network-based approaches usually only rely on texture generation to edit
expressions and largely neglect the motion information. In this work, we
propose a novel end-to-end network that disentangles the task of facial editing
into two steps: a " "motion-editing" step and a "texture-editing" step. In the
"motion-editing" step, we explicitly model facial movement through image
deformation, warping the image into the desired expression. In the
"texture-editing" step, we generate necessary textures, such as teeth and
shading effects, for a photo-realistic result. Our physically-based
task-disentanglement system design allows each step to learn a focused task,
removing the need of generating texture to hallucinate motion. Our system is
trained in a self-supervised manner, requiring no ground truth deformation
annotation. Using Action Units [8] as the representation for facial expression,
our method improves the state-of-the-art facial expression editing performance
in both qualitative and quantitative evaluations.
