mlsanityData to 7 Oct 2026

Self-Supervised Vision Transformer for Enhanced Virtual Clothes Try-On

arXiv 2406.10539 ↗First posted 15 Jun 2024cs.CVcited by 0

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

Abstract (arXiv)

Virtual clothes try-on has emerged as a vital feature in online shopping, offering consumers a critical tool to visualize how clothing fits. In our research, we introduce an innovative approach for virtual clothes try-on, utilizing a self-supervised Vision Transformer (ViT) coupled with a diffusion model. Our method emphasizes detail enhancement by contrasting local clothing image embeddings, generated by ViT, with their global counterparts. Techniques such as conditional guidance and focus on key regions have been integrated into our approach. These combined strategies empower the diffusion model to reproduce clothing details with increased clarity and realism. The experimental results showcase substantial advancements in the realism and precision of details in virtual try-on experiences, significantly surpassing the capabilities of existing technologies.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in 3D, point cloud and scene generation, one of 14 directions in Diffusion models. Builds on 32 of its 39 references that are in the map. First posted on arXiv 15 Jun 2024.

Builds on

32 of 39 references.

Closest ancestors · 2

Same direction, newest first

  1. 2024TexSliders: Diffusion-Based Texture Editing in CLIP Space1 May
  2. 2023LaDI-VTON: Latent Diffusion Textual-Inversion Enhanced Virtual Try-On22 May

Other references · 30

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
  2. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
  3. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  4. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,694
  5. 2018The Unreasonable Effectiveness of Deep Features as a Perceptual Metriccited by 11,029
  6. 2020Denoising Diffusion Implicit Modelscited by 9,655
  7. 2021Emerging Properties in Self-Supervised Vision Transformerscited by 6,893
  8. 2022Hierarchical Text-Conditional Image Generation with CLIP Latentscited by 6,539
  9. 2022Photorealistic Text-to-Image Diffusion Models with Deep Language Understandingcited by 6,322
  10. 2022Generative Adversarial Networkscited by 5,603
  11. 2021GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Modelscited by 3,595
  12. 2021Palette: Image-to-Image Diffusion Modelscited by 1,296
18 more
  1. 2018Demystifying MMD GANscited by 1,268
  2. 2023Attend-and-Excite: Attention-Based Semantic Guidance for Text-to-Image Diffusion Modelscited by 696
  3. 2021ILVR: Conditioning Method for Denoising Diffusion Probabilistic Modelscited by 634
  4. 2022Paint by Example: Exemplar-based Image Editing with Diffusion Modelscited by 489
  5. 2021VITON-HD: High-Resolution Virtual Try-On via Misalignment-Aware Normalizationcited by 287
  6. 2017VITON: An Image-based Virtual Try-on Networkcited by 285
  7. 2018Toward Characteristic-Preserving Image-based Virtual Try-On Networkcited by 192
  8. 2022Sketch-Guided Text-to-Image Diffusion Modelscited by 180
  9. 2022Dress Code: High-Resolution Multi-Category Virtual Try-Oncited by 161
  10. 2020Towards Photo-Realistic Virtual Try-On by Adaptively GeneratingleftrightarrowPreserving Image Contentcited by 148
  11. 2021Parser-Free Virtual Try-on via Distilling Appearance Flowscited by 142
  12. 2022High-Resolution Virtual Try-On with Misalignment and Occlusion-Handled Conditionscited by 129
  13. 2022Style-Based Global Appearance Flow for Virtual Try-Oncited by 88
  14. 2023LayerDiffusion: Layered Controlled Image Editing with Diffusion Modelscited by 40
  15. 2023Towards Garment Sewing Pattern Reconstruction from a Single Imagecited by 33
  16. 2021M3D-VTON: A Monocular-to-3D Virtual Try-On Networkcited by 31
  17. 2023GarmentCode: Programming Parametric Sewing Patternscited by 26
  18. 2023FashionTex: Controllable Virtual Try-on with Text and Texturecited by 9

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  4. 2024Texture-Preserving Diffusion Models for High-Fidelity Virtual Try-On
  5. 2025ITVTON: Virtual Try-On Diffusion Transformer Based on Integrated Image and Text
  6. 2025Once Is Enough: Lightweight DiT-Based Video Virtual Try-On via One-Time Garment Appearance Injection
  7. 2025EfficientVITON: An Efficient Virtual Try-On Model using Optimized Diffusion Process
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