Synthesizing Normalized Faces From Facial Identity Features
Forrester Cole, David Belanger, Dilip Krishnan, Aaron Sarna, Inbar Mosseri, William T. Freeman
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.
BibTeX
@inproceedings{cvpr2017_synthesizingnorm,
title = {Synthesizing Normalized Faces From Facial Identity Features},
author = {Forrester Cole and David Belanger and Dilip Krishnan and Aaron Sarna and Inbar Mosseri and William T. Freeman},
booktitle = {CVPR 2017},
year = {2017}
}