EGGAN: Learning Latent Space for Fine-Grained Expression Manipulation

Fine-grained facial expression aims at changing the expression of an image without altering facial identity. Most current expression manipulation methods are based on a discrete expression label, which mainly manipulates holistic expression with details neglected. To handle the above mentioned problems, we propose an end-to-end expression-guided generative adversarial network (EGGAN), which synthesizes an image with expected expression given continuous expression label and structured latent code. In particular, an adversarial autoencoder is used to translate a source image into a structured latent space. The encoded latent code and the target expression label are input to a conditional GAN to synthesize an image with the target expression. Moreover, a perceptual loss and a multiscale structural similarity loss are introduced to preserve facial identity and global shape during expression manipulation. Extensive experiments demonstrate that our approach can edit fine-grained expressions, and synthesize continuous intermediate expressions between source and target expressions.