Toward Accurate and Realistic Outfits Visualization with Attention to Details
Kedan Li, Min jin Chong, Jeffrey Zhang, Jingen Liu

TL;DR
This paper introduces OVNet, a novel virtual try-on system that captures detailed visual features like buttons and textures, producing realistic multi-garment images with an interactive interface for e-commerce.
Contribution
We propose a new outfit visualization network with a cascade warping approach and outfit-model matching, significantly improving detail accuracy and realism over prior methods.
Findings
Generated images show higher realism and detail quality.
Quantitative analysis confirms superior performance.
Deployed interface received positive user feedback.
Abstract
Virtual try-on methods aim to generate images of fashion models wearing arbitrary combinations of garments. This is a challenging task because the generated image must appear realistic and accurately display the interaction between garments. Prior works produce images that are filled with artifacts and fail 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 between garments) and produce high quality multiple-garment virtual try-on images. OVNet consists of 1) a semantic layout generator and 2) an image generation pipeline using multiple coordinated warps. We train the warper to output multiple warps using a cascade loss, which refines each successive warp to focus on poorly generated regions of a previous warp and…
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Taxonomy
Topics3D Shape Modeling and Analysis · Generative Adversarial Networks and Image Synthesis · Computer Graphics and Visualization Techniques
