Image-to-Image Translation with Conditional Adversarial Networks

Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, Alexei A. Efros
2017 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)  
Labels to Facade BW to Color Aerial to Map Labels to Street Scene Edges to Photo input output input input input input output output output output input output Day to Night Figure 1: Many problems in image processing, graphics, and vision involve translating an input image into a corresponding output image. These problems are often treated with application-specific algorithms, even though the setting is always the same: map pixels to pixels. Conditional adversarial nets are a general-purpose
more » ... general-purpose solution that appears to work well on a wide variety of these problems. Here we show results of the method on several. In each case we use the same architecture and objective, and simply train on different data. Abstract We investigate conditional adversarial networks as a general-purpose solution to image-to-image translation problems. These networks not only learn the mapping from input image to output image, but also learn a loss function to train this mapping. This makes it possible to apply the same generic approach to problems that traditionally would require very different loss formulations. We demonstrate that this approach is effective at synthesizing photos from label maps, reconstructing objects from edge maps, and colorizing images, among other tasks. Moreover, since the release of the pix2pix software associated with this paper, hundreds of twitter users have posted their own artistic experiments using our system. As a community, we no longer hand-engineer our mapping functions, and this work suggests we can achieve reasonable results without handengineering our loss functions either.
doi:10.1109/cvpr.2017.632 dblp:conf/cvpr/IsolaZZE17 fatcat:js4bzcqwljef5kamaictkh4j6q