  The reflectance field of a face describes the reflectance properties
responsible for complex lighting effects including diffuse, specular,
inter-reflection and self shadowing. Most existing methods for estimating the
face reflectance from a monocular image assume faces to be diffuse with very
few approaches adding a specular component. This still leaves out important
perceptual aspects of reflectance as higher-order global illumination effects
and self-shadowing are not modeled. We present a new neural representation for
face reflectance where we can estimate all components of the reflectance
responsible for the final appearance from a single monocular image. Instead of
modeling each component of the reflectance separately using parametric models,
our neural representation allows us to generate a basis set of faces in a
geometric deformation-invariant space, parameterized by the input light
direction, viewpoint and face geometry. We learn to reconstruct this
reflectance field of a face just from a monocular image, which can be used to
render the face from any viewpoint in any light condition. Our method is
trained on a light-stage training dataset, which captures 300 people
illuminated with 150 light conditions from 8 viewpoints. We show that our
method outperforms existing monocular reflectance reconstruction methods, in
terms of photorealism due to better capturing of physical premitives, such as
sub-surface scattering, specularities, self-shadows and other higher-order
effects.

Computer Vision and Pattern Recognition;Graphics;Machine Learning