融合显式与隐式形变，提升虚拟试衣的细节保真与自然度。
HYB-VITON: A Hybrid Approach to Virtual Try-On Combining Explicit and Implicit Warping
- 结合显式形变保细节与隐式重建更自然的优点
- 在保持服装细节上优于扩散模型方法
- 适合追求真实感与细节精度的电商试衣应用
虚拟试衣系统在电子商务中具有重要潜力，使用户能可视化衣物穿在身上的效果。现有基于图像的方法分为两类：直接将衣物图像形变到人体图像（显式形变），以及利用交叉注意力重建给定衣物（隐式形变）。显式形变能保留衣物细节，但常产生不自然结果；隐式形变生成更自然的外观，但难以捕捉精细纹理。本文提出HYB-VITON，一种新方法，融合两种策略的优势，并引入预处理管道和新型训练机制。该设计使模型能利用显式形变中的有效区域，同时借助隐式形变的自然重建能力。实验表明，相比近期扩散模型方法，该方法更忠实保留衣物细节；相较于最先进的显式形变方法，生成结果更具真实性。
原文摘要 · Abstract (English)
Virtual try-on systems have significant potential in e-commerce, allowing customers to visualize garments on themselves. Existing image-based methods fall into two categories: those that directly warp garment-images onto person-images (explicit warping), and those using cross-attention to reconstruct given garments (implicit warping). Explicit warping preserves garment details but often produces unrealistic output, while implicit warping achieves natural reconstruction but struggles with fine details. We propose HYB-VITON, a novel approach that combines the advantages of each method and includes both a preprocessing pipeline for warped garments and a novel training option. These components allow us to utilize beneficial regions of explicitly warped garments while leveraging the natural reconstruction of implicit warping. A series of experiments demonstrates that HYB-VITON preserves garment details more faithfully than recent diffusion-based methods, while producing more realistic results than a state-of-the-art explicit warping method.
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