Tstars-Tryon 1.0: Robust and Realistic Virtual Try-On for Diverse Fashion Items
Mengting Chen, Zhengrui Chen, Yongchao Du, Zuan Gao, Taihang Hu, Jinsong Lan, Chao Lin, Yefeng Shen, Xingjian Wang, Zhao Wang, Zhengtao Wu, Xiaoli Xu, Zhengze Xu, Hao Yan, Mingzhou Zhang, Jun Zheng, Qinye Zhou, Xiaoyong Zhu, Bo Zheng

TL;DR
Tstars-Tryon 1.0 is a comprehensive virtual try-on system that offers high realism, robustness, multi-image support, and near real-time performance, suitable for large-scale commercial deployment.
Contribution
The paper introduces Tstars-Tryon 1.0, a novel virtual try-on system that significantly improves robustness, realism, multi-image flexibility, and inference speed for practical use.
Findings
Achieves high success rate in challenging real-world conditions.
Provides photorealistic results with detailed garment textures.
Deployed at industrial scale serving millions of users.
Abstract
Recent advances in image generation and editing have opened new opportunities for virtual try-on. However, existing methods still struggle to meet complex real-world demands. We present Tstars-Tryon 1.0, a commercial-scale virtual try-on system that is robust, realistic, versatile, and highly efficient. First, our system maintains a high success rate across challenging cases like extreme poses, severe illumination variations, motion blur, and other in-the-wild conditions. Second, it delivers highly photorealistic results with fine-grained details, faithfully preserving garment texture, material properties, and structural characteristics, while largely avoiding common AI-generated artifacts. Third, beyond apparel try-on, our model supports flexible multi-image composition (up to 6 reference images) across 8 fashion categories, with coordinated control over person identity and background.…
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