CrossVTON: Mimicking the Logic Reasoning on Cross-category Virtual Try-on guided by Tri-zone Priors
Donghao Luo, Yujie Liang, Xu Peng, Xiaobin Hu, Boyuan Jiang, Chengming, Xu, Taisong Jin, Chengjie Wang, Yanwei Fu

TL;DR
CrossVTON introduces a structured framework inspired by human reasoning, decomposing model images into three zones and employing tri-zone priors to enhance cross-category virtual try-on realism and robustness.
Contribution
The paper proposes a novel structured reasoning framework with tri-zone priors for improved cross-category virtual try-on, surpassing existing methods in quality and versatility.
Findings
Achieves state-of-the-art results in cross-category try-on tasks.
Effectively handles size mismatches and region-specific garment fitting.
Demonstrates superior qualitative and quantitative performance.
Abstract
Despite remarkable progress in image-based virtual try-on systems, generating realistic and robust fitting images for cross-category virtual try-on remains a challenging task. The primary difficulty arises from the absence of human-like reasoning, which involves addressing size mismatches between garments and models while recognizing and leveraging the distinct functionalities of various regions within the model images. To address this issue, we draw inspiration from human cognitive processes and disentangle the complex reasoning required for cross-category try-on into a structured framework. This framework systematically decomposes the model image into three distinct regions: try-on, reconstruction, and imagination zones. Each zone plays a specific role in accommodating the garment and facilitating realistic synthesis. To endow the model with robust reasoning capabilities for…
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Taxonomy
TopicsSemantic Web and Ontologies · Multi-Agent Systems and Negotiation · Logic, Reasoning, and Knowledge
MethodsALIGN
