PFDM：基于扩散模型的无解析器虚拟试穿
PFDM: Parser-Free Virtual Try-on via Diffusion Model
AI总结：
针对虚拟试穿依赖分割掩码的瓶颈，提出基于扩散模型的无解析器方法PFDM，通过隐式变形和服装融合注意力机制，在扩展数据集上训练，实现高保真试穿并超越现有模型。
摘要：
虚拟试穿可以显著提升线上和线下场景中的服装购物体验，因此在计算机视觉领域引起了广泛关注。然而，为了实现高保真的试穿效果，大多数最先进的方法仍然依赖于精确的分割掩码，这些掩码通常由近乎完美的解析器或人工标注生成。为了克服这一瓶颈，我们提出了一种基于扩散模型的无解析器虚拟试穿方法（PFDM）。给定两张图像，PFDM可以通过隐式变形将服装无缝地“穿戴”到目标人物身上，而无需任何其他信息。为了有效学习该模型，我们合成大量伪图像，并通过将各种服装穿戴到人物身上来构建样本对。在大规模扩展数据集的监督下，我们利用所提出的服装融合注意力（GFA）机制融合人物和服装特征。实验表明，我们提出的PFDM能够成功处理复杂情况，合成高保真图像，并优于最先进的无解析器和基于解析器的模型。
英文摘要：
Virtual try-on can significantly improve the garment shopping experiences in both online and in-store scenarios, attracting broad interest in computer vision. However, to achieve high-fidelity try-on performance, most state-of-the-art methods still rely on accurate segmentation masks, which are often produced by near-perfect parsers or manual labeling. To overcome the bottleneck, we propose a parser-free virtual try-on method based on the diffusion model (PFDM). Given two images, PFDM can "wear" garments on the target person seamlessly by implicitly warping without any other information. To learn the model effectively, we synthesize many pseudo-images and construct sample pairs by wearing various garments on persons. Supervised by the large-scale expanded dataset, we fuse the person and garment features using a proposed Garment Fusion Attention (GFA) mechanism. Experiments demonstrate that our proposed PFDM can successfully handle complex cases, synthesize high-fidelity images, and outperform both state-of-the-art parser-free and parser-based models.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
