  We present a differentiable ray-tracing based novel face reconstruction
approach where scene attributes - 3D geometry, reflectance (diffuse, specular
and roughness), pose, camera parameters, and scene illumination - are estimated
from unconstrained monocular images. The proposed method models scene
illumination via a novel, parameterized virtual light stage, which
in-conjunction with differentiable ray-tracing, introduces a coarse-to-fine
optimization formulation for face reconstruction. Our method can not only
handle unconstrained illumination and self-shadows conditions, but also
estimates diffuse and specular albedos. To estimate the face attributes
consistently and with practical semantics, a two-stage optimization strategy
systematically uses a subset of parametric attributes, where subsequent
attribute estimations factor those previously estimated. For example,
self-shadows estimated during the first stage, later prevent its baking into
the personalized diffuse and specular albedos in the second stage. We show the
efficacy of our approach in several real-world scenarios, where face attributes
can be estimated even under extreme illumination conditions. Ablation studies,
analyses and comparisons against several recent state-of-the-art methods show
improved accuracy and versatility of our approach. With consistent face
attributes reconstruction, our method leads to several style -- illumination,
albedo, self-shadow -- edit and transfer applications, as discussed in the
paper.
