用参考图提升虚拟试衣真实感，无需复杂输入
RefTon: Reference person shot assist virtual Try-on
- 仅需源图和服装图，直接生成试穿效果
- 引入多参考图像，显著提升纹理对齐与细节保留
- 设计简洁高效，适合快速部署的试衣应用
我们提出 RefTon，一种基于 Flux 的人到人虚拟试衣框架，通过非配对视觉参考图增强服装真实感。与依赖复杂辅助输入（如人体分割、扭曲掩码）或精细设计提取分支的传统方法不同，RefTon 直接从源图像和目标服装图生成试穿结果，无需结构引导或辅助组件处理多样输入。受人类选衣行为启发，RefTon 利用目标服装在不同人物身上的参考图像，为纹理对齐和服装细节保持提供强大指导。为此，我们构建了一个包含非配对参考图像的训练数据集。在公开基准上的大量实验表明，RefTon 在性能上达到或优于现有最先进方法，同时保持简单高效的端到端设计。
原文摘要 · Abstract (English)
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.
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