mlsanityData to 14 Oct 2026

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

arXiv 2606.29319 ↗First posted 28 Jun 2026cs.CVcited by 0

On the map: Diffusion models → 3D, point cloud and scene generation

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in 3D, point cloud and scene generation, one of 14 directions in Diffusion models. Builds on 39 of its 46 references that are in the map. First posted on arXiv 28 Jun 2026.

Builds on

39 of 46 references.

Closest ancestors · 8

Same direction, newest first

  1. 2025FastFit: Accelerating Multi-Reference Virtual Try-On via Cacheable Diffusion Models28 Aug
  2. 2025OmniVTON: Training-Free Universal Virtual Try-On20 Jul
  3. 2025ITA-MDT: Image-Timestep-Adaptive Masked Diffusion Transformer Framework for Image-Based Virtual Try-On26 Mar
  4. 2024CatVTON: Concatenation Is All You Need for Virtual Try-On with Diffusion Models21 Jul
  5. 2024Texture-Preserving Diffusion Models for High-Fidelity Virtual Try-On1 Apr
  6. 2024Improving Diffusion Models for Authentic Virtual Try-on in the Wild8 Mar
  7. 2024OOTDiffusion: Outfitting Fusion based Latent Diffusion for Controllable Virtual Try-on4 Mar
  8. 2023StableVITON: Learning Semantic Correspondence with Latent Diffusion Model for Virtual Try-On4 Dec

Other references · 31

  1. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,863
  2. 2018The Unreasonable Effectiveness of Deep Features as a Perceptual Metriccited by 11,177
  3. 2020Denoising Diffusion Implicit Modelscited by 9,781
  4. 2021SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equationscited by 1,937
  5. 2023MasaCtrl: Tuning-Free Mutual Self-Attention Control for Consistent Image Synthesis and Editingcited by 727
  6. 2022On Distillation of Guided Diffusion Modelscited by 725
  7. 2018DensePose: Dense Human Pose Estimation In The Wildcited by 575
  8. 2021VITON-HD: High-Resolution Virtual Try-On via Misalignment-Aware Normalizationcited by 287
  9. 2017VITON: An Image-based Virtual Try-on Networkcited by 286
  10. 2023SnapFusion: Text-to-Image Diffusion Model on Mobile Devices within Two Secondscited by 227
  11. 2024Taming Rectified Flow for Inversion and Editingcited by 204
  12. 2019U-GAT-IT: Unsupervised Generative Attentional Networks with Adaptive Layer-Instance Normalization for Image-to-Image Translationcited by 186
19 more
  1. 2022Dress Code: High-Resolution Multi-Category Virtual Try-Oncited by 162
  2. 2024In-Context LoRA for Diffusion Transformerscited by 158
  3. 2020Towards Photo-Realistic Virtual Try-On by Adaptively GeneratingleftrightarrowPreserving Image Contentcited by 149
  4. 2021Parser-Free Virtual Try-on via Distilling Appearance Flowscited by 143
  5. 2022High-Resolution Virtual Try-On with Misalignment and Occlusion-Handled Conditionscited by 130
  6. 2023TRACT: Denoising Diffusion Models with Transitive Closure Time-Distillationcited by 116
  7. 2024Stable Flow: Vital Layers for Training-Free Image Editingcited by 113
  8. 2023BOOT: Data-free Distillation of Denoising Diffusion Models with Bootstrappingcited by 98
  9. 2024Golden Noise for Diffusion Models: A Learning Frameworkcited by 92
  10. 2022Style-Based Global Appearance Flow for Virtual Try-Oncited by 88
  11. 2020Do Not Mask What You Do Not Need to Mask: a Parser-Free Virtual Try-Oncited by 77
  12. 2024Not All Noises Are Created Equally:Diffusion Noise Selection and Optimizationcited by 71
  13. 2025Any2AnyTryon: Leveraging Adaptive Position Embeddings for Versatile Virtual Clothing Taskscited by 63
  14. 2023Street TryOn: Learning In-the-Wild Virtual Try-On from Unpaired Person Imagescited by 34
  15. 2017Creating Capsule Wardrobes from Fashion Imagescited by 24
  16. 2022OutfitTransformer: Learning Outfit Representations for Fashion Recommendationcited by 24
  17. 2023PICTURE: PhotorealistIC virtual Try-on from UnconstRained dEsignscited by 14
  18. 2025OmniTry: Virtual Try-On Anything without Maskscited by 13
  19. 2026VTEdit-Bench: A Comprehensive Benchmark for Multi-Reference Image Editing Models in Virtual Try-Oncited by 1

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  2. 2025MC-VTON: Minimal Control Virtual Try-On Diffusion Transformer
  3. 2025One Model for All: Unified Try-On and Try-Off in Any Pose via LLM-Inspired Bidirectional Tweedie Diffusion
  4. 2024Fashion-VDM: Video Diffusion Model for Virtual Try-On
  5. 2024DiffusionTrend: A Minimalist Approach to Virtual Fashion Try-On
  6. 2026What Matters in Virtual Try-Off? Dual-UNet Diffusion Model For Garment Reconstruction
  7. 2025JCo-MVTON: Jointly Controllable Multi-Modal Diffusion Transformer for Mask-Free Virtual Try-on
  8. 2025MF-VITON: High-Fidelity Mask-Free Virtual Try-On with Minimal Input