Controllable 3D Generative Adversarial Face Model via Disentangling Shape and Appearance
This work addresses a specific bottleneck in 3D face modeling for applications like virtual avatars and synthetic data generation, representing an incremental improvement.
The paper tackles the problem of lacking control over subtle expressions in 3D face generative models by proposing a new model that decouples identity and expression, enabling granular expression control while preserving identity, as demonstrated with holistic expression and Action Unit labels.
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.