Image-based virtual try-on aims to transfer target in-shop clothing to a
dressed model image, the objectives of which are totally taking off original
clothing while preserving the contents outside of the try-on area, naturally
wearing target clothing and correctly inpainting the gap between target
clothing and original clothing. Tremendous efforts have been made to facilitate
this popular research area, but cannot keep the type of target clothing with
the try-on area affected by original clothing. In this paper, we focus on the
unpaired virtual try-on situation where target clothing and original clothing
on the model are different, i.e., the practical scenario. To break the
correlation between the try-on area and the original clothing and make the
model learn the correct information to inpaint, we propose an adaptive mask
training paradigm that dynamically adjusts training masks. It not only improves
the alignment and fit of clothing but also significantly enhances the fidelity
of virtual try-on experience. Furthermore, we for the first time propose two
metrics for unpaired try-on evaluation, the Semantic-Densepose-Ratio (SDR) and
Skeleton-LPIPS (S-LPIPS), to evaluate the correctness of clothing type and the
accuracy of clothing texture. For unpaired try-on validation, we construct a
comprehensive cross-try-on benchmark (Cross-27) with distinctive clothing items
and model physiques, covering a broad try-on scenarios. Experiments demonstrate
the effectiveness of the proposed methods, contributing to the advancement of
virtual try-on technology and offering new insights and tools for future
research in the field. The code, model and benchmark will be publicly released.

本文提出了一种自适应掩码训练范式，通过破解试穿区域与原始服装之间的关联，并让模型学习正确的信息进行修补，从而在非配对虚拟试穿情况下提高服装的对齐度和适应性，显著提升虚拟试穿体验的真实感；并首次提出两种用于非配对试穿评估的指标，Semantic-Densepose-Ratio (SDR) 和 Skeleton-LPIPS (S-LPIPS)，用于评估服装类型的正确性和纹理的准确性；并构建了一个全面的跨试穿基准测试集（Cross-27），涵盖了多种不同的服装项目和模特身材，用于验证非配对试穿，实验证明了所提方法的有效性，为虚拟试穿技术的发展做出贡献，为该领域的未来研究提供新的洞察和工具。代码、模型和基准测试集将公开发布。