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In this paper we present BioFaceNet, a deep CNN that learns to decompose a single face image into biophysical parameters maps, diffuse and specular shading maps as well as estimating the spectral power distribution of the scene illuminant and the spectral sensitivity of the camera.
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Beyond principal components: Deep Boltzmann machines for face modeling
Chi Nhan Duong, Khoa Luu, Kha Gia Quach, and Tien D Bui · 2015
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U-net: Convolutional networks for biomedical image segmentation
Olaf Ronneberger, Philipp Fischer, and Thomas Brox · 2015
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A biophysical 3d morphable model of face appearance
Sarah Alotaibi and William A. P. Smith · 2017
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Neural face editing with intrinsic image disentangling
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Mofa: Model-based deep convolutional face autoencoder for unsupervised monocular reconstruction
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InverseFaceNet: Deep Single-Shot Inverse Face Rendering From A Single Image
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Intrinsic face image decomposition with human face priors
Chen Li, Kun Zhou, and Stephen Lin · 2014
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Deep learning face attributes in the wild
Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaoou Tang · 2015
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Hyeongwoo Kim, Michael Zollöfer, Ayush Tewari, Justus Thies, Christian Richardt, and Theobalt Christian · 2018
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Sfsnet: Learning shape, refectance and illuminance of faces in the wild
Soumyadip Sengupta, Angjoo Kanazawa, Carlos D. Castillo, and David W. Jacobs · 2018
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Self-supervised multi-level face model learning for monocular reconstruction at over 250 hz
A. Tewari, M. Zollhöfer, P. Garrido, F. Bernard, H. Kim, P. Pérez, and C. Theobalt · 2018
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Decomposing multispectral face images into diffuse and specular shading and biophysical parameters
Sarah Alotaibi and William A. P. Smith · 2019
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