  In the past several decades, many attempts have been made to model synthetic
realistic geometric data. The goal of such models is to generate plausible 3D
geometries and textures. Perhaps the best known of its kind is the linear 3D
morphable model (3DMM) for faces. Such models can be found at the core of many
computer vision applications such as face reconstruction, recognition and
authentication to name just a few.
  Generative adversarial networks (GANs) have shown great promise in imitating
high dimensional data distributions. State of the art GANs are capable of
performing tasks such as image to image translation as well as auditory and
image signal synthesis, producing novel plausible samples from the data
distribution at hand.
  Geometric data is generally more difficult to process due to the inherent
lack of an intrinsic parametrization. By bringing geometric data into an
aligned space, we are able to map the data onto a 2D plane using a universal
parametrization. This alignment process allows for efficient processing of
digitally scanned geometric data via image processing tools.
  Using this methodology, we propose a novel face synthesis model for
generation of realistic facial textures together with their corresponding
geometry. A GAN is employed in order to imitate the space of parametrized human
textures, while corresponding facial geometries are generated by learning the
best 3DMM coefficients for each texture. The generated textures are mapped back
onto the corresponding geometries to obtain new generated high resolution 3D
faces.
