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

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. Fourth, to overcome the latency bottlenecks of commercial deployment, our system is heavily optimized for inference speed, delivering near real-time generation for a seamless user experience. These capabilities are enabled by an integrated system design spanning end-to-end model architecture, a scalable data engine, robust infrastructure, and a multi-stage training paradigm. Extensive evaluation and large-scale product deployment demonstrate that Tstars-Tryon1.0 achieves leading overall performance. To support future research, we also release a comprehensive benchmark. The model has been deployed at an industrial scale on the Taobao App, serving millions of users with tens of millions of requests.