mlsanityData to 7 Oct 2026

Shining Yourself: High-Fidelity Ornaments Virtual Try-on with Diffusion Model

arXiv 2503.16065 ↗First posted 20 Mar 2025cs.CVcited by 6

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

Abstract (arXiv)

While virtual try-on for clothes and shoes with diffusion models has gained attraction, virtual try-on for ornaments, such as bracelets, rings, earrings, and necklaces, remains largely unexplored. Due to the intricate tiny patterns and repeated geometric sub-structures in most ornaments, it is much more difficult to guarantee identity and appearance consistency under large pose and scale variances between ornaments and models. This paper proposes the task of virtual try-on for ornaments and presents a method to improve the geometric and appearance preservation of ornament virtual try-ons. Specifically, we estimate an accurate wearing mask to improve the alignments between ornaments and models in an iterative scheme alongside the denoising process. To preserve structure details, we further regularize attention layers to map the reference ornament mask to the wearing mask in an implicit way. Experimental results demonstrate that our method successfully wears ornaments from reference images onto target models, handling substantial differences in scale and pose while preserving identity and achieving realistic visual effects.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Placed in 3D, point cloud and scene generation, one of 14 directions in Diffusion models. Builds on 39 of its 43 references that are in the map. First posted on arXiv 20 Mar 2025.

Builds on

39 of 43 references.

Closest ancestors · 8

Same direction, newest first

  1. 2024ShoeModel: Learning to Wear on the User-specified Shoes via Diffusion Model7 Apr
  2. 2024Texture-Preserving Diffusion Models for High-Fidelity Virtual Try-On1 Apr
  3. 2024ObjectDrop: Bootstrapping Counterfactuals for Photorealistic Object Removal and Insertion27 Mar
  4. 2024Improving Diffusion Models for Authentic Virtual Try-on in the Wild8 Mar
  5. 2024OOTDiffusion: Outfitting Fusion based Latent Diffusion for Controllable Virtual Try-on4 Mar
  6. 2024DiLightNet: Fine-grained Lighting Control for Diffusion-based Image Generation19 Feb
  7. 2023StableVITON: Learning Semantic Correspondence with Latent Diffusion Model for Virtual Try-On4 Dec
  8. 2023CAT-DM: Controllable Accelerated Virtual Try-on with Diffusion Model30 Nov

Other references · 31

  1. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  2. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
  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. 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,185
  8. 2023Segment Anythingcited by 8,158
  9. 2021Emerging Properties in Self-Supervised Vision Transformerscited by 6,893
  10. 2022Photorealistic Text-to-Image Diffusion Models with Deep Language Understandingcited by 6,322
  11. 2023Adding Conditional Control to Text-to-Image Diffusion Modelscited by 5,466
  12. 2022DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generationcited by 3,286
19 more
  1. 2022Prompt-to-Prompt Image Editing with Cross Attention Controlcited by 2,409
  2. 2022An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversioncited by 2,231
  3. 2023IP-Adapter: Text Compatible Image Prompt Adapter for Text-to-Image Diffusion Modelscited by 1,439
  4. 2023AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuningcited by 1,418
  5. 2022Multi-Concept Customization of Text-to-Image Diffusioncited by 1,099
  6. 2021Blended Diffusion for Text-driven Editing of Natural Imagescited by 924
  7. 2022Plug-and-Play Diffusion Features for Text-Driven Image-to-Image Translationcited by 897
  8. 2023Animate Anyone: Consistent and Controllable Image-to-Video Synthesis for Character Animationcited by 683
  9. 2022Paint by Example: Exemplar-based Image Editing with Diffusion Modelscited by 489
  10. 2024InstantID: Zero-shot Identity-Preserving Generation in Secondscited by 431
  11. 2022Blended Latent Diffusioncited by 430
  12. 2023AnyDoor: Zero-shot Object-level Image Customizationcited by 411
  13. 2023InstantBooth: Personalized Text-to-Image Generation without Test-Time Finetuningcited by 343
  14. 2023SVDiff: Compact Parameter Space for Diffusion Fine-Tuningcited by 338
  15. 2023Inpaint Anything: Segment Anything Meets Image Inpaintingcited by 233
  16. 2023TryOnDiffusion: A Tale of Two UNetscited by 141
  17. 2019DoveNet: Deep Image Harmonization via Domain Verificationcited by 134
  18. 2024Training-Free Consistent Text-to-Image Generationcited by 123
  19. 2024Strictly-ID-Preserved and Controllable Accessory Advertising Image Generationcited by 2

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 2; 2026: 4.

6 papers.

Similar, not cited

Close in wording, no citation either way

  1. 2024Time-Efficient and Identity-Consistent Virtual Try-On Using A Variant of Altered Diffusion Models
  2. 2024M&M VTO: Multi-Garment Virtual Try-On and Editing
  3. 2024Product-Level Try-on: Characteristics-preserving Try-on with Realistic Clothes Shading and Wrinkles
  4. 2025DiffFit: Disentangled Garment Warping and Texture Refinement for Virtual Try-On
  5. 2026What Matters in Virtual Try-Off? Dual-UNet Diffusion Model For Garment Reconstruction
  6. 2024TryOn-Adapter: Efficient Fine-Grained Clothing Identity Adaptation for High-Fidelity Virtual Try-On
  7. 2024Masked Extended Attention for Zero-Shot Virtual Try-On In The Wild
  8. 2024Self-Supervised Vision Transformer for Enhanced Virtual Clothes Try-On