Flow based garment warping is an integral part of image-based virtual try-on networks. However, optimizing a single flow predicting network for simultaneous global boundary alignment and local texture preservation results in sub-optimal flow fields. Moreover, dense flows are inherently not suited to handle intricate conditions like garment occlusion by body parts or by other garments. Forcing flows to handle the above issues results in various distortions like texture squeezing, and stretching. In this work, we propose a novel approach where we disentangle the global boundary alignment and local texture preserving tasks via our GlobalNet and LocalNet modules. A consistency loss is then employed between the two modules which harmonizes the local flows with the global boundary alignment. Additionally, we explicitly handle occlusions by predicting body-parts visibility mask, which is used to mask out the occluded regions in the warped garment. The masking prevents the LocalNet from predicting flows that distort texture to compensate for occlusions. We also introduce a novel regularization loss (NIPR), that defines a criteria to identify the regions in the warped garment where texture integrity is violated (squeezed or stretched). NIPR subsequently penalizes the flow in those regions to ensure regular and coherent warps that preserve the texture in local neighborhoods. Evaluation on a widely used virtual try-on dataset demonstrates strong performance of our network compared to the current SOTA methods.

本文提出了一种新颖的方法，通过全局边界对齐和局部纹理保护模块来解耦全局边界对齐和局部纹理保护任务，并通过两个模块之间的一致性损失来协调局部流场和全局边界对齐。通过预测身体部位的可见性掩模来显式处理遮挡，以防止纹理被扭曲。此外，引入了一种新的正则化损失（NIPR）来惩罚纹理完整性受损的区域，以确保保持局部邻域中的纹理的规则和连贯变形。在广泛使用的虚拟试穿数据集上的评估表明，我们的网络在性能上表现强于当前的SOTA方法。

GC-VTON：全球一致和遮挡感知的局部流预测与邻域完整性保持应用于虚拟试衣