mlsanityData to 7 Oct 2026

Style-NeRF2NeRF: 3D Style Transfer From Style-Aligned Multi-View Images

arXiv 2406.13393 ↗First posted 19 Jun 2024cs.CV cs.GRcited by 16

On the map: Diffusion models → Image generation, text-to-image and editing

Abstract (arXiv)

We propose a simple yet effective pipeline for stylizing a 3D scene, harnessing the power of 2D image diffusion models. Given a NeRF model reconstructed from a set of multi-view images, we perform 3D style transfer by refining the source NeRF model using stylized images generated by a style-aligned image-to-image diffusion model. Given a target style prompt, we first generate perceptually similar multi-view images by leveraging a depth-conditioned diffusion model with an attention-sharing mechanism. Next, based on the stylized multi-view images, we propose to guide the style transfer process with the sliced Wasserstein loss based on the feature maps extracted from a pre-trained CNN model. Our pipeline consists of decoupled steps, allowing users to test various prompt ideas and preview the stylized 3D result before proceeding to the NeRF fine-tuning stage. We demonstrate that our method can transfer diverse artistic styles to real-world 3D scenes with competitive quality. Result videos are also available on our project page: https://haruolabs.github.io/style-n2n/

Key facts. Cited by 16 papers in the map (data through 7 Oct 2026). Placed in Image generation, text-to-image and editing, one of 14 directions in Diffusion models. Builds on 51 of its 63 references that are in the map. First posted on arXiv 19 Jun 2024.

Builds on

51 of 63 references.

Closest ancestors · 4

Same direction, newest first

  1. 2023Adding Conditional Control to Text-to-Image Diffusion Models10 Feb
  2. 2022InstructPix2Pix: Learning to Follow Image Editing Instructions17 Nov
  3. 2022Null-text Inversion for Editing Real Images using Guided Diffusion Models17 Nov
  4. 2022Imagic: Text-Based Real Image Editing with Diffusion Models17 Oct

Other references · 47

  1. 2017Attention Is All You Needcited by 59,932
  2. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
  3. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  4. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,694
  5. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
  6. 2018The Unreasonable Effectiveness of Deep Features as a Perceptual Metriccited by 11,029
  7. 2020Denoising Diffusion Implicit Modelscited by 9,655
  8. 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 8,964
  9. 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,185
  10. 2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,005
  11. 20233D Gaussian Splatting for Real-Time Radiance Field Renderingcited by 6,141
  12. 2022Classifier-Free Diffusion Guidancecited by 5,717
35 more
  1. 2023SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesiscited by 4,290
  2. 2022Instant Neural Graphics Primitives with a Multiresolution Hash Encodingcited by 3,698
  3. 2022DreamFusion: Text-to-3D using 2D Diffusioncited by 3,006
  4. 2016Perceptual Losses for Real-Time Style Transfer and Super-Resolutioncited by 2,678
  5. 2022Prompt-to-Prompt Image Editing with Cross Attention Controlcited by 2,409
  6. 2021Mip-NeRF 360: Unbounded Anti-Aliased Neural Radiance Fieldscited by 2,013
  7. 2017Arbitrary Style Transfer in Real-time with Adaptive Instance Normalizationcited by 1,998
  8. 2021SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equationscited by 1,903
  9. 2021Plenoxels: Radiance Fields without Neural Networkscited by 1,607
  10. 2019Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfercited by 1,484
  11. 2020NeRF in the Wild: Neural Radiance Fields for Unconstrained Photo Collectionscited by 1,230
  12. 2021Direct Voxel Grid Optimization: Super-fast Convergence for Radiance Fields Reconstructioncited by 961
  13. 2022Plug-and-Play Diffusion Features for Text-Driven Image-to-Image Translationcited by 897
  14. 2016FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networkscited by 780
  15. 2023Nerfstudio: A Modular Framework for Neural Radiance Field Developmentcited by 596
  16. 2021Ref-NeRF: Structured View-Dependent Appearance for Neural Radiance Fieldscited by 569
  17. 2023Zero-shot Image-to-Image Translationcited by 503
  18. 2023Instruct-NeRF2NeRF: Editing 3D Scenes with Instructionscited by 482
  19. 2015A Neural Algorithm of Artistic Stylecited by 480
  20. 2017Universal Style Transfer via Feature Transformscited by 352
  21. 2018Learning Blind Video Temporal Consistencycited by 247
  22. 2023Style Aligned Image Generation via Shared Attentioncited by 214
  23. 2023ZipLoRA: Any Subject in Any Style by Effectively Merging LoRAscited by 194
  24. 2017Deep Photo Style Transfercited by 168
  25. 2021StyleGAN-NADA: CLIP-Guided Domain Adaptation of Image Generatorscited by 164
  26. 2022ARF: Artistic Radiance Fieldscited by 147
  27. 2022StylizedNeRF: Consistent 3D Scene Stylization as Stylized NeRF via 2D-3D Mutual Learningcited by 140
  28. 2024Implicit Style-Content Separation using B-LoRAcited by 135
  29. 2022NeRF-Art: Text-Driven Neural Radiance Fields Stylizationcited by 126
  30. 2023Delta Denoising Scorecited by 120
  31. 2023StyleRF: Zero-shot 3D Style Transfer of Neural Radiance Fieldscited by 79
  32. 2024ViCA-NeRF: View-Consistency-Aware 3D Editing of Neural Radiance Fieldscited by 68
  33. 2022Improved Direct Voxel Grid Optimization for Radiance Fields Reconstructioncited by 61
  34. 2023Instruct 3D-to-3D: Text Instruction Guided 3D-to-3D conversioncited by 47
  35. 2023General Image-to-Image Translation with One-Shot Image Guidancecited by 31

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 3; 2025: 8; 2026: 5.

16 papers.

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By area:

  1. 2025Tinker: Diffusion's Gift to 3D--Multi-View Consistent Editing From Sparse Inputs without Per-Scene Optimizationcited by 9
  2. 20253D Stylization via Large Reconstruction Modelcited by 8
  3. 2025StyleSculptor: Zero-Shot Style-Controllable 3D Asset Generation with Texture-Geometry Dual Guidancecited by 8
  4. 2024Generative Object Insertion in Gaussian Splatting with a Multi-View Diffusion Modelcited by 6
  5. 2024ArtNVG: Content-Style Separated Artistic Neighboring-View Gaussian Stylizationcited by 4
  6. 2025Voxify3D: Pixel Art Meets Volumetric Renderingcited by 3
  7. 2025GuideFlow3D: Optimization-Guided Rectified Flow For Appearance Transfercited by 2
  8. 2026Tune-Your-Style: Intensity-tunable 3D Style Transfer with Gaussian Splattingcited by 2
  9. 2026TRACE: High-Fidelity 3D Scene Editing via Tangible Reconstruction and Geometry-Aligned Contextual Video Maskingcited by 2
  10. 2025Improved 3D Scene Stylization via Text-Guided Generative Image Editing with Region-Based Controlcited by 1
  11. 2025Jigsaw3D: Disentangled 3D Style Transfer via Patch Shuffling and Maskingcited by 1
  12. 2025LouvreSAE: Sparse Autoencoders for Interpretable and Controllable Style Transfercited by 1
4 more
  1. 2024Incremental Multi-Scene Modeling via Continual Neural Graphics Primitivescited by 0
  2. 2026Training-Free Instance-Aware 3D Scene Reconstruction and Diffusion-Based View Synthesis from Sparse Imagescited by 0
  3. 2026DReSG: Diffusion Residuals for Stylized Gaussian Splattingcited by 0
  4. 2026Stabilizing Camera-Controlled Novel View Synthesis at Inference Timecited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024Towards Multi-View Consistent Style Transfer with One-Step Diffusion via Vision Conditioning
  2. 2025SSGaussian: Semantic-Aware and Structure-Preserving 3D Style Transfer
  3. 2026Structured 3D Latents Are Surprisingly Powerful: Unleashing Generalizable Style with 2D Diffusion
  4. 2024Diffusion-based Human Motion Style Transfer with Semantic Guidance
  5. 2024DiffuseST: Unleashing the Capability of the Diffusion Model for Style Transfer
  6. 2025Leveraging Diffusion Models for Stylization using Multiple Style Images
  7. 2023Style Injection in Diffusion: A Training-free Approach for Adapting Large-scale Diffusion Models for Style Transfer
  8. 2024D2Styler: Advancing Arbitrary Style Transfer with Discrete Diffusion Methods