  Data-driven generative 3D face models are used to compactly encode facial
shape data into meaningful parametric representations. A desirable property of
these models is their ability to effectively decouple natural sources of
variation, in particular identity and expression. While factorized
representations have been proposed for that purpose, they are still limited in
the variability they can capture and may present modeling artifacts when
applied to tasks such as expression transfer. In this work, we explore a new
direction with Generative Adversarial Networks and show that they contribute to
better face modeling performances, especially in decoupling natural factors,
while also achieving more diverse samples. To train the model we introduce a
novel architecture that combines a 3D generator with a 2D discriminator that
leverages conventional CNNs, where the two components are bridged by a geometry
mapping layer. We further present a training scheme, based on auxiliary
classifiers, to explicitly disentangle identity and expression attributes.
Through quantitative and qualitative results on standard face datasets, we
illustrate the benefits of our model and demonstrate that it outperforms
competing state of the art methods in terms of decoupling and diversity.
