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.

GarmentGAN 是一种使用生成对抗方法进行图像化服装转移的算法，可以根据用户提供的两张图像，合成新的虚拟试衣图片，通过在训练中加入分割地图和人体关键点信息，提升了图像的真实性和效果。