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 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.
@article{arxiv.1701.04851,
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},
journal= {arXiv preprint arXiv:1701.04851},
year = {2017}
}