论文标题
部分可观测时空混沌系统的无模型预测
SizeGAN: Improving Size Representation in Clothing Catalogs
论文作者
论文摘要
在线服装目录缺乏体形和服装尺寸的多样性。品牌通常在一个或两种尺寸的型号上展示衣服，很少包括大尺寸型号。据我们所知，我们的论文介绍了第一种生成新目标大小的服装和模型图像的方法，以解决规模不足的问题。我们的主要技术贡献是一个有条件的生成对抗网络，它以多种分辨率学习变形字段，以实际改变模型和服装的大小。我们的两项用户研究的结果表明，大小的尺寸优于沿三个维度（现实主义，服装忠诚和大小）的替代方法，这对于现实世界的使用都很重要。
Online clothing catalogs lack diversity in body shape and garment size. Brands commonly display their garments on models of one or two sizes, rarely including plus-size models. To our knowledge, our paper presents the first method for generating images of garments and models in a new target size to tackle the size under-representation problem. Our primary technical contribution is a conditional generative adversarial network that learns deformation fields at multiple resolutions to realistically change the size of models and garments. Results from our two user studies show SizeGAN outperforms alternative methods along three dimensions -- realism, garment faithfulness, and size -- which are all important for real world use.
