In the past few years 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 most recent works, differentiable
renderers were employed in order to learn the relationship
between the facial identity features and the parameters of
a 3D morphable model for shape and texture. 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 of the state-of-the-art methods is still
not capable of modelling textures in high fidelity. 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 generator of facial texture in UV space. Then, we
revisit the original 3D Morphable Models (3DMMs) fitting
approaches making use of non-linear optimization to find
the optimal latent parameters that best reconstruct the test
image but under a new perspective. We optimize the parameters
with the supervision of pretrained deep identity features
through our end-to-end differentiable framework. 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.1