  Over the past few years, Generative Adversarial Networks (GANs) have garnered
increased interest among researchers in Computer Vision, with applications
including, but not limited to, image generation, translation, imputation, and
super-resolution. Nevertheless, no GAN-based method has been proposed in the
literature that can successfully represent, generate or translate 3D facial
shapes (meshes). This can be primarily attributed to two facts, namely that (a)
publicly available 3D face databases are scarce as well as limited in terms of
sample size and variability (e.g., few subjects, little diversity in race and
gender), and (b) mesh convolutions for deep networks present several challenges
that are not entirely tackled in the literature, leading to operator
approximations and model instability, often failing to preserve high-frequency
components of the distribution. As a result, linear methods such as Principal
Component Analysis (PCA) have been mainly utilized towards 3D shape analysis,
despite being unable to capture non-linearities and high frequency details of
the 3D face - such as eyelid and lip variations. In this work, we present
3DFaceGAN, the first GAN tailored towards modeling the distribution of 3D
facial surfaces, while retaining the high frequency details of 3D face shapes.
We conduct an extensive series of both qualitative and quantitative
experiments, where the merits of 3DFaceGAN are clearly demonstrated against
other, state-of-the-art methods in tasks such as 3D shape representation,
generation, and translation.
