  The garment transfer problem comprises two tasks: learning to separate a
person's body (pose, shape, color) from their clothing (garment type, shape,
style) and then generating new images of the wearer dressed in arbitrary
garments. We present GarmentGAN, a new algorithm that performs image-based
garment transfer through generative adversarial methods. The GarmentGAN
framework allows users to virtually try-on items before purchase and
generalizes to various apparel types. GarmentGAN requires as input only two
images, namely, a picture of the target fashion item and an image containing
the customer. The output is a synthetic image wherein the customer is wearing
the target apparel. In order to make the generated image look photo-realistic,
we employ the use of novel generative adversarial techniques. GarmentGAN
improves on existing methods in the realism of generated imagery and solves
various problems related to self-occlusions. Our proposed model incorporates
additional information during training, utilizing both segmentation maps and
body key-point information. We show qualitative and quantitative comparisons to
several other networks to demonstrate the effectiveness of this technique.
