论文标题
高分辨率虚拟试验，不对对准和遮挡处理条件
High-Resolution Virtual Try-On with Misalignment and Occlusion-Handled Conditions
论文作者
论文摘要
基于图像的虚拟试验旨在综合一个人穿给定服装的人的图像。为了解决任务，现有的方法扭曲了服装物品，以适合该人的身体，并在将物品与人融合之前，在将物品融合之前生成佩戴该物品的人的细分图。但是，当扭曲和细分生成阶段在没有信息交换的情况下单独运行时，扭曲的衣服与细分图之间的未对准发生了，这导致最终图像中的工件。信息断开还会导致在身体部位遮住的衣服区域附近过度翘曲，即所谓的像素 - 碎片。为了解决问题，我们提出了一个新颖的尝试条件发生器，作为两个阶段的统一模块（即扭曲和分割生成阶段）。条件生成器中新提出的特征融合块实现了信息交换，并且条件发生器不会造成任何未对准或像素 - 平方形的工件。我们还介绍了歧视者的拒绝，从而滤除了不正确的细分图预测并确保虚拟试验框架的性能。高分辨率数据集上的实验表明，我们的模型成功处理了未对准和遮挡，并且明显优于基线。代码可在https://github.com/sangyun884/hr-viton上找到。
Image-based virtual try-on aims to synthesize an image of a person wearing a given clothing item. To solve the task, the existing methods warp the clothing item to fit the person's body and generate the segmentation map of the person wearing the item before fusing the item with the person. However, when the warping and the segmentation generation stages operate individually without information exchange, the misalignment between the warped clothes and the segmentation map occurs, which leads to the artifacts in the final image. The information disconnection also causes excessive warping near the clothing regions occluded by the body parts, so-called pixel-squeezing artifacts. To settle the issues, we propose a novel try-on condition generator as a unified module of the two stages (i.e., warping and segmentation generation stages). A newly proposed feature fusion block in the condition generator implements the information exchange, and the condition generator does not create any misalignment or pixel-squeezing artifacts. We also introduce discriminator rejection that filters out the incorrect segmentation map predictions and assures the performance of virtual try-on frameworks. Experiments on a high-resolution dataset demonstrate that our model successfully handles the misalignment and occlusion, and significantly outperforms the baselines. Code is available at https://github.com/sangyun884/HR-VITON.
