MV-TON: Memory-based Video Virtual Try-on network
Xiaojing Zhong, Zhonghua Wu, Taizhe Tan, Guosheng Lin, Qingyao Wu

TL;DR
MV-TON is a novel memory-based network that enables high-resolution, realistic video virtual try-on without clothing templates, advancing the quality and applicability of virtual fitting systems.
Contribution
The paper introduces MV-TON, a memory-augmented framework that improves video virtual try-on by eliminating the need for clothing templates and enhancing output resolution.
Findings
Outperforms existing video virtual try-on methods in quality.
Generates high-resolution, realistic videos.
Effectively transfers clothes without templates.
Abstract
With the development of Generative Adversarial Network, image-based virtual try-on methods have made great progress. However, limited work has explored the task of video-based virtual try-on while it is important in real-world applications. Most existing video-based virtual try-on methods usually require clothing templates and they can only generate blurred and low-resolution results. To address these challenges, we propose a Memory-based Video virtual Try-On Network (MV-TON), which seamlessly transfers desired clothes to a target person without using any clothing templates and generates high-resolution realistic videos. Specifically, MV-TON consists of two modules: 1) a try-on module that transfers the desired clothes from model images to frame images by pose alignment and region-wise replacing of pixels; 2) a memory refinement module that learns to embed the existing generated frames…
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