mlsanityData to 14 Oct 2026

3DOT: Texture Transfer for 3DGS Objects from a Single Reference Image

arXiv 2503.18853 ↗First posted 24 Mar 2025cs.CVcited by 7

Not on the map. Why?

Abstract (arXiv)

3D texture swapping allows for the customization of 3D object textures, enabling efficient and versatile visual transformations in 3D editing. While no dedicated method exists, adapted 2D editing and text-driven 3D editing approaches can serve this purpose. However, 2D editing requires frame-by-frame manipulation, causing inconsistencies across views, while text-driven 3D editing struggles to preserve texture characteristics from reference images. To tackle these challenges, we introduce 3DSwapping, a 3D texture swapping method that integrates: 1) progressive generation, 2) view-consistency gradient guidance, and 3) prompt-tuned gradient guidance. To ensure view consistency, our progressive generation process starts by editing a single reference image and gradually propagates the edits to adjacent views. Our view-consistency gradient guidance further reinforces consistency by conditioning the generation model on feature differences between consistent and inconsistent outputs. To preserve texture characteristics, we introduce prompt-tuning-based gradient guidance, which learns a token that precisely captures the difference between the reference image and the 3D object. This token then guides the editing process, ensuring more consistent texture preservation across views. Overall, 3DSwapping integrates these novel strategies to achieve higher-fidelity texture transfer while preserving structural coherence across multiple viewpoints. Extensive qualitative and quantitative evaluations confirm that our three novel components enable convincing and effective 2D texture swapping for 3D objects. Code will be available upon acceptance.

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 37 of its 52 references that are in the map. First posted on arXiv 24 Mar 2025.

Builds on

37 of 52 references.

References · 37

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
  2. 2023GPT-4 Technical Reportcited by 18,396
  3. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,364
  4. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,380
  5. 2018The Unreasonable Effectiveness of Deep Features as a Perceptual Metriccited by 11,177
  6. 2021Emerging Properties in Self-Supervised Vision Transformerscited by 6,968
  7. 20233D Gaussian Splatting for Real-Time Radiance Field Renderingcited by 6,226
  8. 2022Classifier-Free Diffusion Guidancecited by 5,847
  9. 2023Adding Conditional Control to Text-to-Image Diffusion Modelscited by 5,503
  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. 2022An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversioncited by 2,245
25 more
  1. 2021Mip-NeRF 360: Unbounded Anti-Aliased Neural Radiance Fieldscited by 2,032
  2. 2017Arbitrary Style Transfer in Real-time with Adaptive Instance Normalizationcited by 2,010
  3. 2023IP-Adapter: Text Compatible Image Prompt Adapter for Text-to-Image Diffusion Modelscited by 1,464
  4. 2022Reproducible scaling laws for contrastive language-image learningcited by 1,173
  5. 2022Plug-and-Play Diffusion Features for Text-Driven Image-to-Image Translationcited by 906
  6. 2023Instruct-NeRF2NeRF: Editing 3D Scenes with Instructionscited by 484
  7. 2023GaussianEditor: Swift and Controllable 3D Editing with Gaussian Splattingcited by 335
  8. 2023A Tale of Two Features: Stable Diffusion Complements DINO for Zero-Shot Semantic Correspondencecited by 301
  9. 2023DragDiffusion: Harnessing Diffusion Models for Interactive Point-based Image Editingcited by 294
  10. 2022NeRF-Art: Text-Driven Neural Radiance Fields Stylizationcited by 126
  11. 2024GaussCtrl: Multi-View Consistent Text-Driven 3D Gaussian Splatting Editingcited by 115
  12. 2024DGE: Direct Gaussian 3D Editing by Consistent Multi-view Editingcited by 94
  13. 2024DiffEditor: Boosting Accuracy and Flexibility on Diffusion-based Image Editingcited by 88
  14. 2022ClipFace: Text-guided Editing of Textured 3D Morphable Modelscited by 60
  15. 2024GOI: Find 3D Gaussians of Interest with an Optimizable Open-vocabulary Semantic-space Hyperplanecited by 53
  16. 2024Director3D: Real-world Camera Trajectory and 3D Scene Generation from Textcited by 50
  17. 2024InstantSwap: Fast Customized Concept Swapping across Sharp Shape Differencescited by 34
  18. 2025Drag Your Gaussian: Effective Drag-Based Editing with Score Distillation for 3D Gaussian Splattingcited by 22
  19. 2024StyleSplat: 3D Object Style Transfer with Gaussian Splattingcited by 18
  20. 2024PartFormer: Awakening Latent Diverse Representation from Vision Transformer for Object Re-Identificationcited by 13
  21. 2023HEAP: Unsupervised Object Discovery and Localization with Contrastive Groupingcited by 10
  22. 2023Class-level Structural Relation Modelling and Smoothing for Visual Representation Learningcited by 9
  23. 2024Towards Better Text-to-Image Generation Alignment via Attention Modulationcited by 9
  24. 2023Attention Disturbance and Dual-Path Constraint Network for Occluded Person Re-identificationcited by 6
  25. 2024SSNeRF: Sparse View Semi-supervised Neural Radiance Fields with Augmentationcited by 6

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 6.

7 papers.