We study the problem of mapping an input image to a tied pair consisting of a vector of parameters and an image that is created using a graphical engine from the vector of parameters. The mapping's objective is to have the output image as similar as possible to the input image. During training, no supervision is given in the form of matching inputs and outputs. This learning problem extends two literature problems: unsupervised domain adaptation and cross domain transfer. We define a generalization bound that is based on discrepancy, and employ a GAN to implement a network solution that corresponds to this bound. Experimentally, our method is shown to solve the problem of automatically creating avatars.
Cite
@article{arxiv.1704.05693,
title = {Unsupervised Creation of Parameterized Avatars},
author = {Lior Wolf and Yaniv Taigman and Adam Polyak},
journal= {arXiv preprint arXiv:1704.05693},
year = {2017}
}
Comments
v2 -- a change in the references due to a request from authors