  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.
