We present an image-conditional image generation model. The model transfers an input domain to a target domain in semantic level, and generates the target image in pixel level. To generate realistic target images, we employ the real/fake-discriminator as in Generative Adversarial Nets, but also introduce a novel domain-discriminator to make the generated image relevant to the input image. We verify our model through a challenging task of generating a piece of clothing from an input image of a dressed person. We present a high quality clothing dataset containing the two domains, and succeed in demonstrating decent results.
@article{arxiv.1603.07442,
title = {Pixel-Level Domain Transfer},
author = {Donggeun Yoo and Namil Kim and Sunggyun Park and Anthony S. Paek and In So Kweon},
journal= {arXiv preprint arXiv:1603.07442},
year = {2016}
}
Comments
Published in ECCV 2016. Code and dataset available at dgyoo.github.io