Abstract
Virtual Try-On technology has garnered significant attention for its potential to transform the online fashion retail experience by allowing users to visualize how garments would look on them without physical trials. While recent advances in diffusion-based warping-free methods have improved perceptual quality, they often fail to preserve fine-grained garment details such as logos and printed text elements that are critical for brand integrity and customer trust. In this work, we propose DualFit, a hybrid VTON pipeline that addresses this limitation by two-stage approach. In the first stage, DualFit warps the target garment to align with the person image using a learned flow field, ensuring high-fidelity preservation. In the second stage, a fidelity-preserving try-on module synthesizes the final output by blending the warped garment with preserved human regions. Particularly, to guide this process, we introduce a preserved-region input and an inpainting mask, enabling the model to retain key areas and regenerate only where necessary, particularly around garment seams. Extensive qualitative results show that DualFit achieves visually seamless try-on results while faithfully maintaining high-frequency garment details, striking an effective balance between reconstruction accuracy and perceptual realism.
Abstract (translated)
虚拟试穿技术因其能够通过允许用户在不进行实体试穿的情况下可视化服装的穿着效果，从而革新在线时尚零售体验而备受关注。尽管基于扩散的方法已经改善了感知质量，但这些方法通常无法保持诸如徽标和印刷文字等细微的服装细节，而这些都是品牌完整性和客户信任的关键因素。为此，我们提出了DualFit技术，这是一种混合虚拟试穿(VTON)流水线，通过两阶段的方法解决了这一限制。 在第一阶段中，DualFit使用学习到的流场将目标服装变形以与人物图像对齐，从而确保高保真度的保存。第二阶段则是一个保持保真度的试穿模块，该模块通过将变形后的服装与保留的人体区域融合来生成最终输出。特别地，为指导这一过程，我们引入了一个保留区域输入和一个修复掩码，使模型能够仅在必要时（例如，在服装接缝周围）保留关键区域并重新生成细节。 大量的定性结果显示，DualFit实现了视觉上无缝的试穿效果，并且忠实地保持了高频次的服装细节，有效平衡了重建准确性和感知真实性。
URL
https://arxiv.org/abs/2508.12131
