TileGAN: category-oriented attention-based high-quality tiled clothes generation from dressed person

During the past decades, applying deep learning technologies on fashion industry are increasingly the mainstream. Due to the different gesture, illumination or self-occasion, it is hard to directly utilize the clothes images in real-world applications. In this paper, to handle this problem, we present a novel multi-stage, category-supervised attention-based conditional generative adversarial network by generating clear and detailed tiled clothing images from certain model images. This newly proposed method consists of two stages: in the first stage, we generate the coarse image which contains general appearance information (such as color and shape) and category of the garment, where a spatial transformation module is utilized to handle the shape changes during image synthesis and an additional classifier is employed to guide coarse image generated in a category-supervised manner; in the second stage, we propose a dual path attention-based model to generate the fine-tuned image, which combines the appearance information of the coarse result with the high-frequency information of the model image. In detail, we introduce the channel attention mechanism to assign weights to the information of different channels instead of connecting directly. Then, a self-attention module is employed to model long-range correlation making the generated image close to the target. In additional to the framework, we also create a person-to-clothing data set containing 10 categories of clothing, which includes more than 34 thousand pairs of images with category attribute. Extensive simulations are conducted, and experimental result on the data set demonstrates the feasibility and superiority of the proposed networks.