  Generative Adversarial Networks (GANs) are currently the method of choice for
generating visual data. Certain GAN architectures and training methods have
demonstrated exceptional performance in generating realistic synthetic images
(in particular, of human faces). However, for 3D object, GANs still fall short
of the success they have had with images. One of the reasons is due to the fact
that so far GANs have been applied as 3D convolutional architectures to
discrete volumetric representations of 3D objects. In this paper, we propose
the first intrinsic GANs architecture operating directly on 3D meshes (named as
MeshGAN). Both quantitative and qualitative results are provided to show that
MeshGAN can be used to generate high-fidelity 3D face with rich identities and
expressions.
