# StyleRF: Zero-shot 3D Style Transfer of Neural Radiance Fields

arXiv: https://arxiv.org/abs/2303.10598 · HTML: https://mlsanity.com/paper/2303.10598/

## Key facts

- Cited by 79 papers in the map (data through 14 Oct 2026).
- Builds on 57 of its 75 references that are in the map.
- First posted on arXiv 19 Mar 2023.

## Builds on (most cited first, 30 of 57)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016)
- [Instant Neural Graphics Primitives with a Multiresolution Hash Encoding](https://mlsanity.com/paper/2201.05989/) (2022)
- [Perceptual Losses for Real-Time Style Transfer and Super-Resolution](https://mlsanity.com/paper/1603.08155/) (2016)
- [RAFT: Recurrent All-Pairs Field Transforms for Optical Flow](https://mlsanity.com/paper/2003.12039/) (2020)
- [Occupancy Networks: Learning 3D Reconstruction in Function Space](https://mlsanity.com/paper/1812.03828/) (2018)
- [Mip-NeRF 360: Unbounded Anti-Aliased Neural Radiance Fields](https://mlsanity.com/paper/2111.12077/) (2021)
- [Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization](https://mlsanity.com/paper/1703.06868/) (2017)
- [Mip-NeRF: A Multiscale Representation for Anti-Aliasing Neural Radiance Fields](https://mlsanity.com/paper/2103.13415/) (2021)
- [Plenoxels: Radiance Fields without Neural Networks](https://mlsanity.com/paper/2112.05131/) (2021)
- [TensoRF: Tensorial Radiance Fields](https://mlsanity.com/paper/2203.09517/) (2022)
- [Efficient Geometry-aware 3D Generative Adversarial Networks](https://mlsanity.com/paper/2112.07945/) (2021)
- [Instance Normalization: The Missing Ingredient for Fast Stylization](https://mlsanity.com/paper/1607.08022/) (2016)
- [Learning Implicit Fields for Generative Shape Modeling](https://mlsanity.com/paper/1812.02822/) (2018)
- [Direct Voxel Grid Optimization: Super-fast Convergence for Radiance Fields Reconstruction](https://mlsanity.com/paper/2111.11215/) (2021)
- [Neural Sparse Voxel Fields](https://mlsanity.com/paper/2007.11571/) (2020)
- [NeRF++: Analyzing and Improving Neural Radiance Fields](https://mlsanity.com/paper/2010.07492/) (2020)
- [GIRAFFE: Representing Scenes as Compositional Generative Neural Feature Fields](https://mlsanity.com/paper/2011.12100/) (2020)
- [Convolutional Occupancy Networks](https://mlsanity.com/paper/2003.04618/) (2020)
- [K-Planes: Explicit Radiance Fields in Space, Time, and Appearance](https://mlsanity.com/paper/2301.10241/) (2023)
- [Differentiable Volumetric Rendering: Learning Implicit 3D Representations without 3D Supervision](https://mlsanity.com/paper/1912.07372/) (2019)
- [GRAF: Generative Radiance Fields for 3D-Aware Image Synthesis](https://mlsanity.com/paper/2007.02442/) (2020)
- [Local Light Field Fusion: Practical View Synthesis with Prescriptive Sampling Guidelines](https://mlsanity.com/paper/1905.00889/) (2019)
- [Pixel2Mesh: Generating 3D Mesh Models from Single RGB Images](https://mlsanity.com/paper/1804.01654/) (2018)
- [Point-NeRF: Point-based Neural Radiance Fields](https://mlsanity.com/paper/2201.08845/) (2022)
- [StyleNeRF: A Style-based 3D-Aware Generator for High-resolution Image Synthesis](https://mlsanity.com/paper/2110.08985/) (2021)
- [In-Place Scene Labelling and Understanding with Implicit Scene Representation](https://mlsanity.com/paper/2103.15875/) (2021)

## Built on by (most cited first, 30 of 79)

- [GaussCtrl: Multi-View Consistent Text-Driven 3D Gaussian Splatting Editing](https://mlsanity.com/paper/2403.08733/) (2024)
- [StyleGaussian: Instant 3D Style Transfer with Gaussian Splatting](https://mlsanity.com/paper/2403.07807/) (2024)
- [VideoMV: Consistent Multi-View Generation Based on Large Video Generative Model](https://mlsanity.com/paper/2403.12010/) (2024)
- [3D Paintbrush: Local Stylization of 3D Shapes with Cascaded Score Distillation](https://mlsanity.com/paper/2311.09571/) (2023)
- [Novel View Extrapolation with Video Diffusion Priors](https://mlsanity.com/paper/2411.14208/) (2024)
- [WaveNeRF: Wavelet-based Generalizable Neural Radiance Fields](https://mlsanity.com/paper/2308.04826/) (2023)
- [StylizedGS: Controllable Stylization for 3D Gaussian Splatting](https://mlsanity.com/paper/2404.05220/) (2024)
- [ED-NeRF: Efficient Text-Guided Editing of 3D Scene with Latent Space NeRF](https://mlsanity.com/paper/2310.02712/) (2023)
- [SGSST: Scaling Gaussian Splatting StyleTransfer](https://mlsanity.com/paper/2412.03371/) (2024)
- [Geometry Transfer for Stylizing Radiance Fields](https://mlsanity.com/paper/2402.00863/) (2024)
- [Gaussian Splatting in Style](https://mlsanity.com/paper/2403.08498/) (2024)
- [Style-NeRF2NeRF: 3D Style Transfer From Style-Aligned Multi-View Images](https://mlsanity.com/paper/2406.13393/) (2024)
- [Lift3D: Zero-Shot Lifting of Any 2D Vision Model to 3D](https://mlsanity.com/paper/2403.18922/) (2024)
- [CoARF: Controllable 3D Artistic Style Transfer for Radiance Fields](https://mlsanity.com/paper/2404.14967/) (2024)
- [AI-Generated Images as Data Source: The Dawn of Synthetic Era](https://mlsanity.com/paper/2310.01830/) (2023)
- [Localized Gaussian Splatting Editing with Contextual Awareness](https://mlsanity.com/paper/2408.00083/) (2024)
- [Reference-based Controllable Scene Stylization with Gaussian Splatting](https://mlsanity.com/paper/2407.07220/) (2024)
- [NeRF Analogies: Example-Based Visual Attribute Transfer for NeRFs](https://mlsanity.com/paper/2402.08622/) (2024)
- [InstantStyleGaussian: Efficient Art Style Transfer with 3D Gaussian Splatting](https://mlsanity.com/paper/2408.04249/) (2024)
- [Dreamer XL: Towards High-Resolution Text-to-3D Generation via Trajectory Score Matching](https://mlsanity.com/paper/2405.11252/) (2024)
- [StyleRF-VolVis: Style Transfer of Neural Radiance Fields for Expressive Volume Visualization](https://mlsanity.com/paper/2408.00150/) (2024)
- [MVGBench: Comprehensive Benchmark for Multi-view Generation Models](https://mlsanity.com/paper/2507.00006/) (2025)
- [Tinker: Diffusion's Gift to 3D--Multi-View Consistent Editing From Sparse Inputs without Per-Scene Optimization](https://mlsanity.com/paper/2508.14811/) (2025)
- [Advances in 3D Neural Stylization: A Survey](https://mlsanity.com/paper/2311.18328/) (2023)
- [SpectralGaussians: Semantic, spectral 3D Gaussian splatting for multi-spectral scene representation, visualization and analysis](https://mlsanity.com/paper/2408.06975/) (2024)
- [CLIPGaussian: Universal and Multimodal Style Transfer Based on Gaussian Splatting](https://mlsanity.com/paper/2505.22854/) (2025)
- [ABC-GS: Alignment-Based Controllable Style Transfer for 3D Gaussian Splatting](https://mlsanity.com/paper/2503.22218/) (2025)
- [ARF-Plus: Controlling Perceptual Factors in Artistic Radiance Fields for 3D Scene Stylization](https://mlsanity.com/paper/2308.12452/) (2023)
- [StyleMe3D: Stylization with Disentangled Priors by Multiple Encoders on 3D Gaussians](https://mlsanity.com/paper/2504.15281/) (2025)
- [LAENeRF: Local Appearance Editing for Neural Radiance Fields](https://mlsanity.com/paper/2312.09913/) (2023)

## Abstract (arXiv)

3D style transfer aims to render stylized novel views of a 3D scene with multi-view consistency. However, most existing work suffers from a three-way dilemma over accurate geometry reconstruction, high-quality stylization, and being generalizable to arbitrary new styles. We propose StyleRF (Style Radiance Fields), an innovative 3D style transfer technique that resolves the three-way dilemma by performing style transformation within the feature space of a radiance field. StyleRF employs an explicit grid of high-level features to represent 3D scenes, with which high-fidelity geometry can be reliably restored via volume rendering. In addition, it transforms the grid features according to the reference style which directly leads to high-quality zero-shot style transfer. StyleRF consists of two innovative designs. The first is sampling-invariant content transformation that makes the transformation invariant to the holistic statistics of the sampled 3D points and accordingly ensures multi-view consistency. The second is deferred style transformation of 2D feature maps which is equivalent to the transformation of 3D points but greatly reduces memory footprint without degrading multi-view consistency. Extensive experiments show that StyleRF achieves superior 3D stylization quality with precise geometry reconstruction and it can generalize to various new styles in a zero-shot manner.