Synthesizing Normalized Faces From Facial Identity Features

Forrester Cole, David Belanger, Dilip Krishnan, Aaron Sarna, Inbar Mosseri, William T. Freeman; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, pp. 3703-3712

Abstract


We present a method for synthesizing a frontal, neutral-expression image of a person's face, given an input face photograph. This is achieved by learning to generate facial landmarks and textures from features extracted from a facial-recognition network. Unlike previous generative approaches, our encoding feature vector is largely invariant to lighting, pose, and facial expression. Exploiting this invariance, we train our decoder network using only frontal, neutral-expression photographs. Since these photographs are well aligned, we can decompose them into a sparse set of landmark points and aligned texture maps. The decoder then predicts landmarks and textures independently and combines them using a differentiable image warping operation. The resulting images can be used for a number of applications, such as analyzing facial attributes, exposure and white balance adjustment, or creating a 3-D avatar.

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[bibtex]
@InProceedings{Cole_2017_CVPR,
author = {Cole, Forrester and Belanger, David and Krishnan, Dilip and Sarna, Aaron and Mosseri, Inbar and Freeman, William T.},
title = {Synthesizing Normalized Faces From Facial Identity Features},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {July},
year = {2017}
}