PROMO：面向高效高保真虚拟试衣的可提示换装
PROMO: Promptable Outfitting for Efficient High-Fidelity Virtual Try-On
把虚拟试衣重新表述为结构化图像编辑问题，用 Flow Matching DiT 骨干网络加潜空间多模态条件拼接，并通过自参考机制减少采样步数与推理开销，为需要强主体保持与纹理迁移的可控生成任务提供了可复用的条件注入与加速思路。
原文依据 We present PROMO, a promptable virtual try-on framework built upon a Flow Matching DiT backbone with latent multi-modal conditional concatenation.
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论虚拟试衣（VTON）已成为在线零售的核心能力，逼真的试衣结果能够提供可靠的合身指导、减少退货，并使消费者与商家双方受益。基于扩散模型的 VTON 方法能够实现照片级真实感的合成，但往往依赖辅助参考网络等复杂架构，且采样速度慢，使得保真度与效率之间的权衡成为长期难题。我们将 VTON 视为一个结构化图像编辑问题，它需要在三个关键要求下实现强条件生成：主体保持、忠实的纹理迁移以及无缝融合。我们提出 PROMO，一个可提示的虚拟试衣框架，构建于带有潜变量多模态条件拼接的流匹配 DiT 骨干网络之上。借助条件效率与自参考机制，我们的方法大幅降低了推理开销。在标准基准上，PROMO 在视觉保真度上超越了此前的 VTON 方法与通用图像编辑模型，同时在质量与速度之间取得了有竞争力的平衡。这些结果表明，流匹配 Transformer 结合潜变量多模态条件与自参考加速，为高质量虚拟试衣提供了一种有效且训练高效的解决方案。
英文原文摘要 1317 字符
Virtual Try-on (VTON) has become a core capability for online retail, where realistic try-on results provide reliable fit guidance, reduce returns, and benefit both consumers and merchants. Diffusion-based VTON methods achieve photorealistic synthesis, yet often rely on intricate architectures such as auxiliary reference networks and suffer from slow sampling, making the trade-off between fidelity and efficiency a persistent challenge.We approach VTON as a structured image editing problem that demands strong conditional generation under three key requirements: subject preservation, faithful texture transfer, and seamless harmonization. We present PROMO, a promptable virtual try-on framework built upon a Flow Matching DiT backbone with latent multi-modal conditional concatenation. By leveraging conditioning efficiency and self-reference mechanisms, our approach substantially reduces inference overhead. On standard benchmarks, PROMO surpasses both prior VTON methods and general image editing models in visual fidelity while delivering a competitive balance between quality and speed. These results demonstrate that flow-matching transformers, coupled with latent multi-modal conditioning and self-reference acceleration, offer an effective and training-efficient solution for high-quality virtual try-on.