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Virtual try-on methods aim to generate images of fashion models wearingarbitrary combinations of garments. This is a challenging task because thegenerated image must appear realistic and accurately display the interactionbetween garments. Prior works produce images that are filled with artifacts andfail to capture important visual details necessary for commercial applications.We propose Outfit Visualization Net (OVNet) to capture these important details(e.g. buttons, shading, textures, realistic hemlines, and interactions betweengarments) and produce high quality multiple-garment virtual try-on images.OVNet consists of 1) a semantic layout generator and 2) an image generationpipeline using multiple coordinated warps. We train the warper to outputmultiple warps using a cascade loss, which refines each successive warp tofocus on poorly generated regions of a previous warp and yields consistentimprovements in detail. In addition, we introduce a method for matching outfitswith the most suitable model and produce significant improvements for both ourand other previous try-on methods. Through quantitative and qualitativeanalysis, we demonstrate our method generates substantially higher-qualitystudio images compared to prior works for multi-garment outfits. An interactiveinterface powered by this method has been deployed on fashion e-commercewebsites and received overwhelmingly positive feedback.

Toward Accurate and Realistic Outfits Visualization with Attention to Details