TL;DR
This paper introduces a pixel-level domain transfer model that generates realistic images in a target domain from an input image, using adversarial training and a novel domain discriminator, demonstrated on clothing images.
Contribution
The paper proposes a new image generation model that transfers images between domains at the pixel level with a domain relevance discriminator, advancing domain transfer techniques.
Findings
Successfully generated clothing images from input images of dressed persons.
Created a high-quality clothing dataset for domain transfer tasks.
Achieved realistic and relevant image generation results.
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.
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