With the increasing popularity of online shopping, searching for products with images for item retrieval has gradually become an effective approach. This trend is especially evident in the fashion industry. In common media, clothing items are usually worn on the human body. They can be straightforwardly segmented from the source media by utilizing detection or parsing algorithms. However, this may be deleterious to retrieval performance due to distortion, occlusion, and different backgrounds. In this article, a stepwise translation framework using generative adversarial network and thin plate spline is developed to transfer human body images to tiled clothing images, which can be directly used for clothing retrieval. Experimental results demonstrate the effectiveness of the resultant tiled images produced from our framework in comparison to other extant methods.

中文翻译：

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WarpClothingOut：从人体到平铺图像的衣服转换的逐步框架

随着在线购物的日益普及，搜索带有图像的产品以进行项目检索已逐渐成为一种有效的方法。这种趋势在时装界尤其明显。在普通媒体中，衣物通常穿着在人体上。通过使用检测或解析算法，可以从源媒体中直接对它们进行分段。但是，由于失真，遮挡和背景不同，这可能对检索性能有害。在本文中，开发了使用生成对抗网络和薄板样条的逐步翻译框架，以将人体图像转换为平铺的服装图像，该图像可直接用于服装检索。