LAUSR

Recent years have witnessed the proliferation of online fashion blogs and communities, where a large amount of fashion model images with chic clothes in various scenarios are publicly available. To facilitate users to find the corresponding clothes, we focus on studying how to generate pure wellshaped clothing items with the best view from the complex model images. Towards this end, inspired by painting, where the initial sketches and following coloring are both essential, we propose a two-stage shape-constrained clothing generative framework, dubbed as PaintNet. PaintNet comprises two coherent components: shape predictor and texture renderer. The shape predictor is devised to predict the intermediate shape map for the to-be-generated clothing item based on the latent representation learning, while the texture renderer is introduced to generate the final clothing image with the guidance of the predicted shape map. Extensive qualitative and quantitative experiments conducted on the public Lookbook dataset verify the effectiveness of PaintNet in clothing generation from fashion model images. Moreover, we also explore the potential of PaintNet in the task of cross-domain clothing retrieval, and the experiment results show that PaintNet can achieve, on average, 5.34% performance improvement over the traditional non-generative retrieval methods.