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

Controllable 3D Generative Adversarial Face Model via Disentangling Shape and Appearance