  A lot of work has been done towards reconstructing the 3D facial structure
from single images by capitalizing on the power of Deep Convolutional Neural
Networks (DCNNs). In the recent works, the texture features either correspond
to components of a linear texture space or are learned by auto-encoders
directly from in-the-wild images. In all cases, the quality of the facial
texture reconstruction is still not capable of modeling facial texture with
high-frequency details. In this paper, we take a radically different approach
and harness the power of Generative Adversarial Networks (GANs) and DCNNs in
order to reconstruct the facial texture and shape from single images. That is,
we utilize GANs to train a very powerful facial texture prior \edit{from a
large-scale 3D texture dataset}. Then, we revisit the original 3D Morphable
Models (3DMMs) fitting making use of non-linear optimization to find the
optimal latent parameters that best reconstruct the test image but under a new
perspective. In order to be robust towards initialisation and expedite the
fitting process, we propose a novel self-supervised regression based approach.
We demonstrate excellent results in photorealistic and identity preserving 3D
face reconstructions and achieve for the first time, to the best of our
knowledge, facial texture reconstruction with high-frequency details.
