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. As a community, we no longer hand-engineer our mapping functions, and this work suggests we can achieve reasonable results without hand-engineering our loss functions either.

Source: Image-to-Image Translation with Conditional Adversarial Networks

This is the pix2pix implementation in Tensorflow

The above link also has concrete examples allowing you to play with the data yourself on level = easy.

Image-to-Image Demo
Interactive Image Translation with pix2pix-tensorflow
Written by Christopher Hesse — February 19th, 2017

This is the man who made the tensorflow port and also uses it to fill in drawings of cats.