Abstract
VTON (Virtual Try-ON) aims at synthesizing the target clothing on a certain person, preserving the details of the target clothing while keeping the rest of the person unchanged. Existing methods suffer from the discrepancies between the generated clothing results and the target ones, in terms of the patterns, textures and boundaries. Therefore, we propose to use an energy function to impose constraints on the attention map extracted through the generation process. Thus, at each generation step, the attention can be more focused on the clothing region of interest, thereby influencing the generation results to be more consistent with the target clothing details. Furthermore, to address the limitation that existing evaluation metrics concentrate solely on image realism and overlook the alignment with target elements, we design a new metric, Virtual Try-on Inception Distance (VTID), to bridge this gap and ensure a more comprehensive assessment. On the VITON-HD and DressCode datasets, our approach has outperformed the previous state-of-the-art (SOTA) methods by 1.4%, 2.3%, 12.3%, and 5.8% in the traditional metrics of LPIPS, FID, KID, and the new VTID metrics, respectively. Additionally, by applying the generated data to downstream Clothing-Change Re-identification (CC-Reid) methods, we have achieved performance improvements of 2.5%, 1.1%, and 1.6% on the LTCC, PRCC, VC-Clothes datasets in the metrics of Rank-1. The code of our method is public at this https URL.
Abstract (translated)
VTON（虚拟试穿）旨在将目标衣物合成为特定人物身上的效果，同时保留目标衣物的细节而不改变人体其他部分。现有方法在生成衣物结果与目标衣物之间的模式、纹理和边界方面存在不一致的问题。因此，我们提出使用能量函数来对通过生成过程提取的关注图施加约束条件。这样，在每次生成步骤中，注意力可以更集中于感兴趣的服装区域，从而影响生成的结果更加符合目标衣物的细节。 此外，为了解决现有评估指标仅关注图像现实性而忽视与目标元素对齐的问题，我们设计了一个新的度量标准——虚拟试穿Inception距离（VTID），以此来弥合这一差距，并确保更全面的评估。在VITON-HD和DressCode数据集上，我们的方法分别在传统的LPIPS、FID、KID指标以及新推出的VTID指标上优于之前的最先进（SOTA）方法1.4%、2.3%、12.3%和5.8%。此外，在下游的服装变化再识别（CC-Reid）方法中应用生成的数据，我们在LTCC、PRCC、VC-Clothes数据集上的Rank-1指标分别取得了2.5%、1.1%和1.6%的性能提升。 我们的方法代码可在[此处](https://example.com)获得。
URL
https://arxiv.org/abs/2512.07126
