FDM-MFVT: Few-step Sampling Diffusion Model for Mask-Free Virtual Try-On

• URL: http://arxiv.org/abs/2606.29319v1
• Date: Sun, 28 Jun 2026 10:24:45 GMT
• Title: FDM-MFVT: Few-step Sampling Diffusion Model for Mask-Free Virtual Try-On
• Abstract summary: We propose FDM-MFVT, a few-step diffusion model for mask-free IVTON.<n>The IDT module uses virtual try-on prompts and efficient adaptation to generate high-quality results from garment and person images alone.<n>Experiments show that FDM-MFVT achieves superior quantitative and qualitative results with fewer inference steps than mask-based and mask-free baseline methods.
• Score: 46.87401050104062
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Image-based Virtual Try-On (IVTON) has greatly advanced through diffusion models, yet existing methods require many sampling steps and depend on masks with costly auxiliary networks. In addition, the absence of large-scale mask-free paired datasets further limits the development of mask-free IVTON. We propose FDM-MFVT, a few-step diffusion model for mask-free IVTON, integrating an Outfit-aware Noise Optimization Module (OANO) and an Instruction-driven Try-on Module (IDT) to enhance efficiency and flexibility.The OANO module initializes the alignment space with noise using the input image and only needs 6 steps to generate a higher-fidelity try-on image compared to 30 steps.The IDT module uses virtual try-on prompts and efficient adaptation to generate high-quality results from garment and person images alone. We further introduce MFVT, a 30,000-pair mask-free IVTON dataset. Experiments show that FDM-MFVT achieves superior quantitative and qualitative results with fewer inference steps than mask-based and mask-free baseline methods.

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