Abstract
Existing image-based virtual try-on methods directly transfer specific clothing to a human image without utilizing clothing attributes to refine the transferred clothing geometry and textures, which causes incomplete and blurred clothing appearances. In addition, these methods usually mask the limb textures of the input for the clothing-agnostic person representation, which results in inaccurate predictions for human limb regions (i.e., the exposed arm skin), especially when transforming between long-sleeved and short-sleeved garments. To address these problems, we present a progressive virtual try-on framework, named PL-VTON, which performs pixel-level clothing warping based on multiple attributes of clothing and embeds explicit limb-aware features to generate photo-realistic try-on results. Specifically, we design a Multi-attribute Clothing Warping (MCW) module that adopts a two-stage alignment strategy based on multiple attributes to progressively estimate pixel-level clothing displacements. A Human Parsing Estimator (HPE) is then introduced to semantically divide the person into various regions, which provides structural constraints on the human body and therefore alleviates texture bleeding between clothing and limb regions. Finally, we propose a Limb-aware Texture Fusion (LTF) module to estimate high-quality details in limb regions by fusing textures of the clothing and the human body with the guidance of explicit limb-aware features. Extensive experiments demonstrate that our proposed method outperforms the state-of-the-art virtual try-on methods both qualitatively and quantitatively. The code is available at this https URL.
Abstract (translated)
现有的基于图像的虚拟试穿方法直接将特定服装转移到人体图像上，而不利用衣物属性来精炼转移后的服装几何和纹理信息，这导致了不完整且模糊的服装外观。此外，这些方法通常会遮蔽输入肢体的纹理以获得与衣服无关的人体表示，从而在预测人类肢体区域（如露出的手臂皮肤）时产生不准确的结果，尤其是在长袖和短袖服装转换之间更为明显。为了应对这些问题，我们提出了一种渐进式虚拟试穿框架PL-VTON，该框架基于多属性进行像素级的衣物变形，并嵌入明确的人体部位感知特征以生成逼真的试穿结果。 具体来说，我们设计了一个名为Multi-attribute Clothing Warping (MCW)模块，采用基于多个属性的两阶段对齐策略来逐步估计像素级别的服装位移。接着引入了Human Parsing Estimator（HPE），将人体语义性地划分为不同的区域，从而对人体结构提供约束，并因此减轻了衣物与肢体区域之间的纹理溢出现象。 最后，我们提出了一种Limb-aware Texture Fusion (LTF)模块，在融合服装和人体部位的纹理时使用明确的人体部位感知特征来估计高细节质量的肢体区域信息。广泛的实验表明，我们提出的方法在定性和定量上都优于现有的虚拟试穿方法。相关代码可在提供的链接中获取。
URL
https://arxiv.org/abs/2503.12588
