论文标题
Pasta-Gan ++：高分辨率未配对虚拟试验的多功能框架
PASTA-GAN++: A Versatile Framework for High-Resolution Unpaired Virtual Try-on
论文作者
论文摘要
基于图像的虚拟试验是以人为中心的巨大现实潜力，是以人为中心形象生成的最有希望的应用之一。在这项工作中，我们迈出了一步，探索多功能的虚拟尝试解决方案，我们认为这应该具有三个主要属性，即，它们应支持无监督的培训，任意服装类别和可控制的服装编辑。为此，我们提出了一个特征性的端到端网络，即用空间适应性的gan ++（Pasta-gan ++），以实现一种用于高分辨率不合规的虚拟尝试的多功能系统。具体而言，我们的意大利面++由一个创新的贴布贴片的拆卸模块组成，可以将完整的服装切换为标准化的贴剂，该贴片能够保留服装风格的信息，同时消除服装空间信息，从而减轻在未受监督训练期间过度适应的问题。此外，Pasta-GAN ++引入了基于补丁的服装表示和一个贴片引导的解析合成块，从而使其可以处理任意服装类别并支持本地服装编辑。最后，为了获得具有逼真的纹理细节的尝试结果，面食++结合了一种新型的空间自适应残差模块，以将粗糙的翘曲服装特征注入发电机。对我们新收集的未配对的虚拟试验（UPT）数据集进行了广泛的实验，证明了面食gan ++比现有SOTA的优越性及其具有可控服装编辑的能力。
Image-based virtual try-on is one of the most promising applications of human-centric image generation due to its tremendous real-world potential. In this work, we take a step forwards to explore versatile virtual try-on solutions, which we argue should possess three main properties, namely, they should support unsupervised training, arbitrary garment categories, and controllable garment editing. To this end, we propose a characteristic-preserving end-to-end network, the PAtch-routed SpaTially-Adaptive GAN++ (PASTA-GAN++), to achieve a versatile system for high-resolution unpaired virtual try-on. Specifically, our PASTA-GAN++ consists of an innovative patch-routed disentanglement module to decouple the intact garment into normalized patches, which is capable of retaining garment style information while eliminating the garment spatial information, thus alleviating the overfitting issue during unsupervised training. Furthermore, PASTA-GAN++ introduces a patch-based garment representation and a patch-guided parsing synthesis block, allowing it to handle arbitrary garment categories and support local garment editing. Finally, to obtain try-on results with realistic texture details, PASTA-GAN++ incorporates a novel spatially-adaptive residual module to inject the coarse warped garment feature into the generator. Extensive experiments on our newly collected UnPaired virtual Try-on (UPT) dataset demonstrate the superiority of PASTA-GAN++ over existing SOTAs and its ability for controllable garment editing.
