GO-MLVTON: Garment Occlusion-Aware Multi-Layer Virtual Try-On with Diffusion Models

• URL: http://arxiv.org/abs/2601.13524v2
• Date: Thu, 22 Jan 2026 08:26:07 GMT
• Title: GO-MLVTON: Garment Occlusion-Aware Multi-Layer Virtual Try-On with Diffusion Models
• Authors: Yang Yu, Yunze Deng, Yige Zhang, Yanjie Xiao, Youkun Ou, Wenhao Hu, Mingchao Li, Bin Feng, Wenyu Liu, Dandan Zheng, Jingdong Chen,
• Abstract summary: Existing image-based virtual try-on (VTON) methods primarily focus on single-layer or multi-garment VTON.<n>We propose GO-MLVTON, the first multi-layer VTON method, introducing the Garment Occlusion Learning module and the StableDiffusion-based Garment Morphing & Fitting module.<n>We present the MLG dataset for this task and propose a new metric named Layered Appearance Coherence Difference (LACD) for evaluation.
• Score: 37.32099831689131
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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/.

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