Monocular Reconstruction of Neural Face Reflectance Fields - Mallikarjun, B. R.; Tewari, Ayush; Oh, Tae-Hyun; Weyrich, Tim; Bickel, Bernd; Seidel, Hans-Peter; Pfister, Hanspeter; Matusik, Wojciech; Elgharib, Mohamed; Theobalt, Christian (Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2021)
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 such as higher-order global illumination effects and self-shadowing. 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 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 dataset, which captures 300 people illuminated with 150 light conditions from 8 viewpoints. We show that our method outperforms existing monocular reflectance reconstruction methods due to better capturing of physical effects, such as sub-surface scattering, specularities, self-shadows and other higher-order effects.
CRIS-Team Scopus Importer:2022-07-26