mlsanityData to 14 Oct 2026

DiffusionTrend: A Minimalist Approach to Virtual Fashion Try-On

arXiv 2412.14465 ↗First posted 19 Dec 2024cs.CVcited by 0

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

Abstract (arXiv)

We introduce DiffusionTrend for virtual fashion try-on, which forgoes the need for retraining diffusion models. Using advanced diffusion models, DiffusionTrend harnesses latent information rich in prior information to capture the nuances of garment details. Throughout the diffusion denoising process, these details are seamlessly integrated into the model image generation, expertly directed by a precise garment mask crafted by a lightweight and compact CNN. Although our DiffusionTrend model initially demonstrates suboptimal metric performance, our exploratory approach offers some important advantages: (1) It circumvents resource-intensive retraining of diffusion models on large datasets. (2) It eliminates the necessity for various complex and user-unfriendly model inputs. (3) It delivers a visually compelling try-on experience, underscoring the potential of training-free diffusion model. This initial foray into the application of untrained diffusion models in virtual try-on technology potentially paves the way for further exploration and refinement in this industrially and academically valuable field.

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 53 of its 76 references that are in the map. First posted on arXiv 19 Dec 2024.

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53 of 76 references.

Closest ancestors · 5

Same direction, newest first

  1. 2024OOTDiffusion: Outfitting Fusion based Latent Diffusion for Controllable Virtual Try-on4 Mar
  2. 2023WarpDiffusion: Efficient Diffusion Model for High-Fidelity Virtual Try-on6 Dec
  3. 2023StableVITON: Learning Semantic Correspondence with Latent Diffusion Model for Virtual Try-On4 Dec
  4. 2023Taming the Power of Diffusion Models for High-Quality Virtual Try-On with Appearance Flow11 Aug
  5. 2023LaDI-VTON: Latent Diffusion Textual-Inversion Enhanced Virtual Try-On22 May

Other references · 48

  1. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  2. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
  3. 2020Denoising Diffusion Probabilistic Modelscited by 21,076
  4. 2018The Unreasonable Effectiveness of Deep Features as a Perceptual Metriccited by 11,177
  5. 2020Denoising Diffusion Implicit Modelscited by 9,781
  6. 2023Segment Anythingcited by 8,234
  7. 2017Mask R-CNNcited by 7,277
  8. 2023SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesiscited by 4,356
  9. 2018Neural Ordinary Differential Equationscited by 3,779
  10. 2022DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generationcited by 3,300
  11. 2022InstructPix2Pix: Learning to Follow Image Editing Instructionscited by 2,611
  12. 2022Prompt-to-Prompt Image Editing with Cross Attention Controlcited by 2,431
36 more
  1. 2022An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversioncited by 2,245
  2. 2018Demystifying MMD GANscited by 1,293
  3. 2022Null-text Inversion for Editing Real Images using Guided Diffusion Modelscited by 1,179
  4. 2022Imagic: Text-Based Real Image Editing with Diffusion Modelscited by 1,178
  5. 2022Multi-Concept Customization of Text-to-Image Diffusioncited by 1,106
  6. 2016Realtime Multi-Person 2D Pose Estimation using Part Affinity Fieldscited by 1,064
  7. 2023MasaCtrl: Tuning-Free Mutual Self-Attention Control for Consistent Image Synthesis and Editingcited by 727
  8. 2018DensePose: Dense Human Pose Estimation In The Wildcited by 575
  9. 2022Paint by Example: Exemplar-based Image Editing with Diffusion Modelscited by 489
  10. 2021VITON-HD: High-Resolution Virtual Try-On via Misalignment-Aware Normalizationcited by 287
  11. 2017VITON: An Image-based Virtual Try-on Networkcited by 286
  12. 2023Q-Diffusion: Quantizing Diffusion Modelscited by 273
  13. 2022Post-training Quantization on Diffusion Modelscited by 267
  14. 2016View Synthesis by Appearance Flowcited by 172
  15. 2022Dress Code: High-Resolution Multi-Category Virtual Try-Oncited by 162
  16. 2020Towards Photo-Realistic Virtual Try-On by Adaptively GeneratingleftrightarrowPreserving Image Contentcited by 149
  17. 2022StyleGAN-Human: A Data-Centric Odyssey of Human Generationcited by 144
  18. 2021Parser-Free Virtual Try-on via Distilling Appearance Flowscited by 143
  19. 2023TryOnDiffusion: A Tale of Two UNetscited by 141
  20. 2022High-Resolution Virtual Try-On with Misalignment and Occlusion-Handled Conditionscited by 130
  21. 2024ReNoise: Real Image Inversion Through Iterative Noisingcited by 128
  22. 2023GP-VTON: Towards General Purpose Virtual Try-on via Collaborative Local-Flow Global-Parsing Learningcited by 120
  23. 2017Look into Person: Self-supervised Structure-sensitive Learning and A New Benchmark for Human Parsingcited by 109
  24. 2023FreeInit: Bridging Initialization Gap in Video Diffusion Modelscited by 95
  25. 2023Effective Real Image Editing with Accelerated Iterative Diffusion Inversioncited by 79
  26. 2020Do Not Mask What You Do Not Need to Mask: a Parser-Free Virtual Try-Oncited by 77
  27. 2022Single Stage Virtual Try-on via Deformable Attention Flowscited by 65
  28. 2024LD-Pruner: Efficient Pruning of Latent Diffusion Models using Task-Agnostic Insightscited by 65
  29. 2022DCCF: Deep Comprehensible Color Filter Learning Framework for High-Resolution Image Harmonizationcited by 46
  30. 2024Wear-Any-Way: Manipulable Virtual Try-on via Sparse Correspondence Alignmentcited by 43
  31. 2023Image-Based Virtual Try-On: A Surveycited by 19
  32. 2021SPG-VTON: Semantic Prediction Guidance for Multi-pose Virtual Try-oncited by 12
  33. 2023A Two-stage Personalized Virtual Try-on Framework with Shape Control and Texture Guidancecited by 10
  34. 2024EasyInv: Toward Fast and Better DDIM Inversioncited by 8
  35. 2023PG-VTON: A Novel Image-Based Virtual Try-On Method via Progressive Inference Paradigmcited by 7
  36. 2021Template-Free Try-on Image Synthesis via Semantic-guided Optimizationcited by 4

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  3. 2024Improving Virtual Try-On with Garment-focused Diffusion Models
  4. 2024Texture-Preserving Diffusion Models for High-Fidelity Virtual Try-On
  5. 2025One Model for All: Unified Try-On and Try-Off in Any Pose via LLM-Inspired Bidirectional Tweedie Diffusion
  6. 2026What Matters in Virtual Try-Off? Dual-UNet Diffusion Model For Garment Reconstruction
  7. 2025ITVTON: Virtual Try-On Diffusion Transformer Based on Integrated Image and Text
  8. 2025FastFit: Accelerating Multi-Reference Virtual Try-On via Cacheable Diffusion Models