Existing image-based virtual try-on (VTON) methods primarily focus on single-layer or multi-garment VTON, neglecting multi-layer VTON (ML-VTON), which involves dressing multiple layers of garments onto the human body with realistic deformation and layering to generate visually plausible outcomes. The main challenge lies in accurately modeling occlusion relationships between inner and outer garments to reduce interference from redundant inner garment features. To address this, we propose GO-MLVTON, the first multi-layer VTON method, introducing the Garment Occlusion Learning module to learn occlusion relationships and the StableDiffusion-based Garment Morphing & Fitting module to deform and fit garments onto the human body, producing high-quality multi-layer try-on results. Additionally, we present the MLG dataset for this task and propose a new metric named Layered Appearance Coherence Difference (LACD) for evaluation. Extensive experiments demonstrate the state-of-the-art performance of GO-MLVTON. Project page: https://upyuyang.github.io/go-mlvton/.

翻译：现有的基于图像的虚拟试穿方法主要关注单层或多件服装的试穿，而忽略了多层虚拟试穿。多层虚拟试穿涉及将多层服装穿着到人体上，并实现真实的形变与层次叠加，以生成视觉上合理的结果。其主要挑战在于准确建模内外层服装之间的遮挡关系，以减少冗余内层服装特征的干扰。为此，我们提出了首个多层虚拟试穿方法GO-MLVTON，引入了服装遮挡学习模块以学习遮挡关系，以及基于StableDiffusion的服装形变与拟合模块，用于将服装变形并贴合到人体上，从而生成高质量的多层试穿结果。此外，我们为此任务构建了MLG数据集，并提出了一种名为分层外观一致性差异的新评估指标。大量实验证明了GO-MLVTON的先进性能。项目页面：https://upyuyang.github.io/go-mlvton/。