  3D face modeling has been an active area of research in computer vision and
computer graphics, fueling applications ranging from facial expression transfer
in virtual avatars to synthetic data generation. Existing 3D deep learning
generative models (e.g., VAE, GANs) allow generating compact face
representations (both shape and texture) that can model non-linearities in the
shape and appearance space (e.g., scatter effects, specularities, etc.).
However, they lack the capability to control the generation of subtle
expressions. This paper proposes a new 3D face generative model that can
decouple identity and expression and provides granular control over
expressions. In particular, we propose using a pair of supervised auto-encoder
and generative adversarial networks to produce high-quality 3D faces, both in
terms of appearance and shape. Experimental results in the generation of 3D
faces learned with holistic expression labels, or Action Unit labels, show how
we can decouple identity and expression; gaining fine-control over expressions
while preserving identity.
