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.

翻译：近年来，图像生成与编辑技术的进展为虚拟试穿开辟了新的可能性。然而，现有方法仍难以满足复杂的现实需求。我们提出Tstars-Tryon 1.0，一个具备鲁棒性、真实感、通用性及高效性的商业级虚拟试穿系统。首先，该系统在极端姿态、剧烈光照变化、运动模糊等复杂野外场景下均保持高成功率。其次，其生成结果高度逼真且细节丰富，能够忠实保留服装纹理、材质属性及结构特征，同时基本避免常见的人工智能生成伪影。第三，除服装试穿外，本模型支持跨8个时尚品类的灵活多图像合成（最多6张参考图像），并可对人身份与背景实现协调控制。第四，为克服商业部署的时延瓶颈，我们针对推理速度进行了深度优化，实现近乎实时的生成，以提供无缝用户体验。这些能力源于涵盖端到端模型架构、可扩展数据引擎、稳健基础设施及多阶段训练范式的集成系统设计。大量评估与规模化产品部署表明，Tstars-Tryon 1.0实现了业界领先的综合性能。为支持未来研究，我们还发布了综合性基准测试。该模型已在淘宝APP上实现工业级部署，服务数百万用户并处理数千万次请求。