Pixel-Level Domain Transfer
Abstract
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.
Cite
Text
Yoo et al. "Pixel-Level Domain Transfer." European Conference on Computer Vision, 2016. doi:10.1007/978-3-319-46484-8_31Markdown
[Yoo et al. "Pixel-Level Domain Transfer." European Conference on Computer Vision, 2016.](https://mlanthology.org/eccv/2016/yoo2016eccv-pixel/) doi:10.1007/978-3-319-46484-8_31BibTeX
@inproceedings{yoo2016eccv-pixel,
title = {{Pixel-Level Domain Transfer}},
author = {Yoo, Donggeun and Kim, Namil and Park, Sunggyun and Paek, Anthony S. and Kweon, In-So},
booktitle = {European Conference on Computer Vision},
year = {2016},
pages = {517-532},
doi = {10.1007/978-3-319-46484-8_31},
url = {https://mlanthology.org/eccv/2016/yoo2016eccv-pixel/}
}