论文标题
Garmentgan：照片现实的对抗性时尚转移
GarmentGAN: Photo-realistic Adversarial Fashion Transfer
论文作者
论文摘要
服装转移问题包括两个任务：学习将一个人的身体（姿势，形状，颜色）与衣服（服装类型，形状，风格）分开，然后生成穿着任意服装的佩戴者的新图像。我们提出了Garmentgan，这是一种新算法，该算法通过生成对抗方法执行基于图像的服装转移。 Garmentgan框架允许用户在购买前几乎可以尝试尝试，并将其推广到各种服装类型。 Garmentgan仅需要输入两张图像，即目标时尚项目的图片和包含客户的图像。输出是综合图像，其中客户穿着目标服装。为了使生成的图像看起来像真实性，我们使用了新颖的生成对抗技术。 Garmentgan改善了生成图像的现实主义现有方法，并解决了与自我估计有关的各种问题。我们提出的模型在培训过程中包含了其他信息，并利用分割图和人体密钥点信息。我们与其他几个网络展示了定性和定量比较，以证明该技术的有效性。
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.
