  We propose DiscoFaceGAN, an approach for face image generation of virtual
people with disentangled, precisely-controllable latent representations for
identity of non-existing people, expression, pose, and illumination. We embed
3D priors into adversarial learning and train the network to imitate the image
formation of an analytic 3D face deformation and rendering process. To deal
with the generation freedom induced by the domain gap between real and rendered
faces, we further introduce contrastive learning to promote disentanglement by
comparing pairs of generated images. Experiments show that through our
imitative-contrastive learning, the factor variations are very well
disentangled and the properties of a generated face can be precisely
controlled. We also analyze the learned latent space and present several
meaningful properties supporting factor disentanglement. Our method can also be
used to embed real images into the disentangled latent space. We hope our
method could provide new understandings of the relationship between physical
properties and deep image synthesis.
