DualFit通过两阶段流程，精准保留服装细节并实现自然试穿效果。
DualFit: A Two-Stage Virtual Try-On via Warping and Synthesis
- 分两阶段：先形变对齐，再融合合成，保持服装细节
- 在真实试穿图像中保留了高频率的商标和印花细节
- 适合关注品牌一致性与视觉真实感的电商应用
虚拟试穿技术有望革新线上时尚零售体验，让用户无需实体试穿即可预览服装上身效果。尽管基于扩散模型的无形变方法在感知质量上取得进展，但常无法保留徽标、印刷文字等精细服装特征，影响品牌完整性与用户信任。本文提出DualFit，一种两阶段混合式虚拟试穿框架。第一阶段利用学习到的光流场将目标服装形变对齐至人体图像，确保高保真度。第二阶段通过保真度保持的试穿模块，融合形变后的服装与原始人体区域，生成最终结果。特别地，引入保留区域输入与修补掩码，引导模型仅在必要区域（如衣缝处）再生内容，从而有效保留关键区域。大量定性结果显示，DualFit在视觉无缝的同时忠实保留高频率服装细节，实现了重建精度与感知真实感的良好平衡。
原文摘要 · Abstract (English)
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.
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