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Data-driven generative 3D face models are used to compactly encode facialshape data into meaningful parametric representations. A desirable property ofthese models is their ability to effectively decouple natural sources ofvariation, in particular identity and expression. While factorizedrepresentations have been proposed for that purpose, they are still limited inthe variability they can capture and may present modeling artifacts whenapplied to tasks such as expression transfer. In this work, we explore a newdirection with Generative Adversarial Networks and show that they contribute tobetter face modeling performances, especially in decoupling natural factors,while also achieving more diverse samples. To train the model we introduce anovel architecture that combines a 3D generator with a 2D discriminator thatleverages conventional CNNs, where the two components are bridged by a geometrymapping layer. We further present a training scheme, based on auxiliaryclassifiers, to explicitly disentangle identity and expression attributes.Through quantitative and qualitative results on standard face datasets, weillustrate the benefits of our model and demonstrate that it outperformscompeting state of the art methods in terms of decoupling and diversity.

A Decoupled 3D Facial Shape Model by Adversarial Training