  Over the last years, many face analysis tasks have accomplished astounding
performance, with applications including face generation and 3D face
reconstruction from a single "in-the-wild" image. Nevertheless, to the best of
our knowledge, there is no method which can produce render-ready
high-resolution 3D faces from "in-the-wild" images and this can be attributed
to the: (a) scarcity of available data for training, and (b) lack of robust
methodologies that can successfully be applied on very high-resolution data. In
this work, we introduce the first method that is able to reconstruct
photorealistic render-ready 3D facial geometry and BRDF from a single
"in-the-wild" image. We capture a large dataset of facial shape and
reflectance, which we have made public. We define a fast facial photorealistic
differentiable rendering methodology with accurate facial skin diffuse and
specular reflection, self-occlusion and subsurface scattering approximation.
With this, we train a network that disentangles the facial diffuse and specular
BRDF components from a shape and texture with baked illumination, reconstructed
with a state-of-the-art 3DMM fitting method. Our method outperforms the
existing arts by a significant margin and reconstructs high-resolution 3D faces
from a single low-resolution image, that can be rendered in various
applications, and bridge the uncanny valley.
