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Deep learning based virtual try-on system has achieved some encouragingprogress recently, but there still remain several big challenges that need tobe solved, such as trying on arbitrary clothes of all types, trying on theclothes from one category to another and generating image-realistic resultswith few artifacts. To handle this issue, we in this paper first collect a newdataset with all types of clothes, \ie tops, bottoms, and whole clothes, eachone has multiple categories with rich information of clothing characteristicssuch as patterns, logos, and other details. Based on this dataset, we thenpropose the Arbitrary Virtual Try-On Network (AVTON) that is utilized forall-type clothes, which can synthesize realistic try-on images by preservingand trading off characteristics of the target clothes and the reference person.Our approach includes three modules: 1) Limbs Prediction Module, which isutilized for predicting the human body parts by preserving the characteristicsof the reference person. This is especially good for handling cross-categorytry-on task (\eg long sleeves \(\leftrightarrow\) short sleeves or long pants\(\leftrightarrow\) skirts, \etc), where the exposed arms or legs with the skincolors and details can be reasonably predicted; 2) Improved Geometric MatchingModule, which is designed to warp clothes according to the geometry of thetarget person. We improve the TPS based warping method with a compactlysupported radial function (Wendland's \(\Psi\)-function); 3) Trade-Off FusionModule, which is to trade off the characteristics of the warped clothes and thereference person. This module is to make the generated try-on images look morenatural and realistic based on a fine-tune symmetry of the network structure.Extensive simulations are conducted and our approach can achieve betterperformance compared with the state-of-the-art virtual try-on methods.

Arbitrary Virtual Try-On Network: Characteristics Preservation and Trade-off between Body and Clothing