RefTon：参考人物照片辅助的虚拟试衣
RefTon: Reference person shot assist virtual Try-on
用无配对参考图（同一件衣服穿在不同人身上）替代 body parsing 与 warped mask 等结构引导，为条件生成提供纹理对齐信号；做可控人像生成或试衣时可借鉴其「参考图即条件」的简化输入范式与配套数据集构建方式。
原文依据 RefTon leverages additional reference images (the target garment worn on different individuals) to provide powerful guidance for refining texture alignment
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论我们提出 RefTon，一个基于 flux 的人到人虚拟试衣框架，通过无配对视觉参考提升服装真实感。与依赖人体解析、变形掩码等复杂辅助输入，或需要精心设计提取分支来处理多种输入条件的传统方法不同，RefTon 简化了流程，直接从源图像和目标服装生成试衣结果，无需结构引导，也无需处理多样输入的辅助组件。此外，受人类挑选衣物行为的启发，RefTon 利用额外的参考图像（目标服装穿在不同个体身上的样子）为细化纹理对齐和保持服装细节提供有力引导。为实现这一能力，我们构建了一个包含无配对参考图像的数据集用于训练。在公开基准上的大量实验表明，RefTon 在保持简洁高效的人到人设计的同时，取得了与最优方法相当或更优的性能。代码与数据集见：https://github.com/360CVGroup/RefTon。
英文原文摘要 1173 字符
We introduce RefTon, a flux-based person-to-person virtual try-on framework that enhances garment realism through unpaired visual references. Unlike conventional approaches that rely on complex auxiliary inputs such as body parsing and warped mask or require finely designed extract branches to process various input conditions, RefTon streamlines the process by directly generating try-on results from a source image and a target garment, without the need for structural guidance or auxiliary components to handle diverse inputs. Moreover, inspired by human clothing selection behavior, RefTon leverages additional reference images (the target garment worn on different individuals) to provide powerful guidance for refining texture alignment and maintaining the garment details. To enable this capability, we built a dataset containing unpaired reference images for training. Extensive experiments on public benchmarks demonstrate that RefTon achieves competitive or superior performance compared to state-of-the-art methods, while maintaining a simple and efficient person-to-person design. \thanks Code and dataset is available at: https://github.com/360CVGroup/RefTon.