@inproceedings{9eb5a6f44f404345925ea87a491c5e1c,
title = "Unsupervised Creation of Parameterized Avatars",
abstract = "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.",
author = "Lior Wolf and Yaniv Taigman and Adam Polyak",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 16th IEEE International Conference on Computer Vision, ICCV 2017 ; Conference date: 22-10-2017 Through 29-10-2017",
year = "2017",
month = dec,
day = "22",
doi = "https://doi.org/10.1109/ICCV.2017.170",
language = "الإنجليزيّة",
series = "Proceedings of the IEEE International Conference on Computer Vision",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1539--1547",
booktitle = "Proceedings - 2017 IEEE International Conference on Computer Vision, ICCV 2017",
address = "الولايات المتّحدة",
}