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[Monocular Reconstruction of Neural Face Reflectance Fields]

Monocular Reconstruction of Neural Face Reflectance Fields

Mallikarjun B R1,  Ayush Tewari1,  Tae-Hyun Oh2,  Tim Weyrich3,  Bernd Bickel4,  Hans-Peter Seidel1,  Hanspeter Pfister5,  Wojciech Matusik6,  Mohamed Elgharib1,  Christian Theobalt1

1 Max Planck Institute for Informatics, Saarbrücken, Germany
2 POSTECH
3 University College London
4 Institute for Science and Technology Austria
5 Harvard University
6 Massachusetts Institute of Technology

Abstract

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 primitives, such as sub-surface scattering, specularities, self-shadows and other higher-order effects.

Citation Style:    Publication

Monocular Reconstruction of Neural Face Reflectance Fields.
Mallikarjun B R, Ayush Tewari, Tae-Hyun Oh, Tim Weyrich, Bernd Bickel, Hans-Peter Seidel, Hanspeter Pfister, Wojciech Matusik, Mohamed Elgharib, Christian Theobalt.
Proc. of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2021.Mallikarjun B R, Ayush Tewari, Tae-Hyun Oh, Tim Weyrich, Bernd Bickel, Hans-Peter Seidel, Hanspeter Pfister, Wojciech Matusik, Mohamed Elgharib, and Christian Theobalt. Monocular reconstruction of neural face reflectance fields. In Proc. of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2021.B R, M., Tewari, A., Oh, T.-H., Weyrich, T., Bickel, B., Seidel, H.-P., Pfister, H., Matusik, W., Elgharib, M., and Theobalt, C. 2021. Monocular reconstruction of neural face reflectance fields. InProc. of IEEE Conference on Computer Vision and Pattern Recognition (CVPR).M. B R, A. Tewari, T.-H. Oh, T. Weyrich, B. Bickel, H.-P. Seidel, H. Pfister, W. Matusik, M. Elgharib, and C. Theobalt, “Monocular reconstruction of neural face reflectance fields,” in Proc. of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Jun. 2021.
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Related Publications and Reports

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Acknowledgments

This work was supported by the ERC Consolidator Grant 4DReply (770784).


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