BooW-VTON：通过无掩码伪数据训练提升自然场景虚拟试穿
BooW-VTON: Boosting In-the-Wild Virtual Try-On via Mask-Free Pseudo Data Training
AI总结：
本文提出BooW-VTON，一种无掩码虚拟试穿扩散模型，通过伪数据训练、自然场景数据增强和试穿定位损失，在无需解析成本的低要求输入下实现自然场景中最先进的试穿质量。
摘要：
基于图像的虚拟试穿是一项日益流行且重要的任务，旨在为特定人物生成逼真的试穿图像。近期方法将虚拟试穿建模为图像掩码修复任务，这需要对人物图像进行掩码处理，导致空间信息的大量丢失。尤其是在具有复杂姿态和遮挡的自然场景试穿中，基于掩码的方法往往会产生明显的伪影。我们的研究发现，无掩码方法能够充分利用原始人物图像中的空间和光照信息，从而实现高质量的虚拟试穿。因此，我们提出了一种用于无掩码试穿扩散模型的新型训练范式。我们通过构建高质量的伪数据来确保模型的无掩码试穿能力，并通过有效的自然场景数据增强进一步增强其对复杂空间信息的处理能力。此外，我们设计了一种试穿定位损失，用于聚焦试穿区域，同时抑制非试穿区域的服装特征，确保服装的精确渲染以及前景/背景的保留。最后，我们提出了BooW-VTON，一种无掩码虚拟试穿扩散模型，无需解析成本即可提供最先进的试穿质量。大量定性和定量实验表明，在如此低要求的输入条件下，该方法在自然场景中展现出卓越的性能。
英文摘要：
Image-based virtual try-on is an increasingly popular and important task to generate realistic try-on images of the specific person. Recent methods model virtual try-on as image mask-inpaint task, which requires masking the person image and results in significant loss of spatial information. Especially, for in-the-wild try-on scenarios with complex poses and occlusions, mask-based methods often introduce noticeable artifacts. Our research found that a mask-free approach can fully leverage spatial and lighting information from the original person image, enabling high-quality virtual try-on. Consequently, we propose a novel training paradigm for a mask-free try-on diffusion model. We ensure the model's mask-free try-on capability by creating high-quality pseudo-data and further enhance its handling of complex spatial information through effective in-the-wild data augmentation. Besides, a try-on localization loss is designed to concentrate on try-on area while suppressing garment features in non-try-on areas, ensuring precise rendering of garments and preservation of fore/back-ground. In the end, we introduce BooW-VTON, the mask-free virtual try-on diffusion model, which delivers SOTA try-on quality without parsing cost. Extensive qualitative and quantitative experiments have demonstrated superior performance in wild scenarios with such a low-demand input.
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。
