  Fine-grained facial expression manipulation is a challenging problem, as
fine-grained expression details are difficult to be captured. Most existing
expression manipulation methods resort to discrete expression labels, which
mainly edit global expressions and ignore the manipulation of fine details. To
tackle this limitation, we propose an end-to-end expression-guided generative
adversarial network (EGGAN), which utilizes structured latent codes and
continuous expression labels as input to generate images with expected
expressions. Specifically, we adopt an adversarial autoencoder to map a source
image into a structured latent space. Then, given the source latent code and
the target expression label, we employ a conditional GAN to generate a new
image with the target expression. Moreover, we introduce a perceptual loss and
a multi-scale structural similarity loss to preserve identity and global shape
during generation. Extensive experiments show that our method can manipulate
fine-grained expressions, and generate continuous intermediate expressions
between source and target expressions.
