# Locally Stylized Neural Radiance Fields

arXiv: https://arxiv.org/abs/2309.10684 · HTML: https://mlsanity.com/paper/2309.10684/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 36 references that are in the map.
- First posted on arXiv 19 Sep 2023.

## Builds on (most cited first, 25 of 25)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [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)
- [Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization](https://mlsanity.com/paper/1703.06868/) (2017)
- [NeuS: Learning Neural Implicit Surfaces by Volume Rendering for Multi-view Reconstruction](https://mlsanity.com/paper/2106.10689/) (2021)
- [Instance Normalization: The Missing Ingredient for Fast Stylization](https://mlsanity.com/paper/1607.08022/) (2016)
- [Neural Sparse Voxel Fields](https://mlsanity.com/paper/2007.11571/) (2020)
- [The Replica Dataset: A Digital Replica of Indoor Spaces](https://mlsanity.com/paper/1906.05797/) (2019)
- [NeRF++: Analyzing and Improving Neural Radiance Fields](https://mlsanity.com/paper/2010.07492/) (2020)
- [Local Light Field Fusion: Practical View Synthesis with Prescriptive Sampling Guidelines](https://mlsanity.com/paper/1905.00889/) (2019)
- [Neural 3D Mesh Renderer](https://mlsanity.com/paper/1711.07566/) (2017)
- [Universal Style Transfer via Feature Transforms](https://mlsanity.com/paper/1705.08086/) (2017)
- [Deep Photo Style Transfer](https://mlsanity.com/paper/1703.07511/) (2017)
- [ARF: Artistic Radiance Fields](https://mlsanity.com/paper/2206.06360/) (2022)
- [StylizedNeRF: Consistent 3D Scene Stylization as Stylized NeRF via 2D-3D Mutual Learning](https://mlsanity.com/paper/2205.12183/) (2022)
- [NeRF-Art: Text-Driven Neural Radiance Fields Stylization](https://mlsanity.com/paper/2212.08070/) (2022)
- [Visual Attribute Transfer through Deep Image Analogy](https://mlsanity.com/paper/1705.01088/) (2017)
- [Stylizing 3D Scene via Implicit Representation and HyperNetwork](https://mlsanity.com/paper/2105.13016/) (2021)
- [Learning to Stylize Novel Views](https://mlsanity.com/paper/2105.13509/) (2021)
- [Unsupervised Learning of Image Segmentation Based on Differentiable Feature Clustering](https://mlsanity.com/paper/2007.09990/) (2020)
- [A Style-Aware Content Loss for Real-time HD Style Transfer](https://mlsanity.com/paper/1807.10201/) (2018)
- [StyleMesh: Style Transfer for Indoor 3D Scene Reconstructions](https://mlsanity.com/paper/2112.01530/) (2021)
- [Interactive Video Stylization Using Few-Shot Patch-Based Training](https://mlsanity.com/paper/2004.14489/) (2020)

## Built on by (most cited first, 20 of 20)

- [InFusion: Inpainting 3D Gaussians via Learning Depth Completion from Diffusion Prior](https://mlsanity.com/paper/2404.11613/) (2024)
- [StylizedGS: Controllable Stylization for 3D Gaussian Splatting](https://mlsanity.com/paper/2404.05220/) (2024)
- [Geometry Transfer for Stylizing Radiance Fields](https://mlsanity.com/paper/2402.00863/) (2024)
- [NeRF Analogies: Example-Based Visual Attribute Transfer for NeRFs](https://mlsanity.com/paper/2402.08622/) (2024)
- [Language-driven Object Fusion into Neural Radiance Fields with Pose-Conditioned Dataset Updates](https://mlsanity.com/paper/2309.11281/) (2023)
- [Advances in 3D Neural Stylization: A Survey](https://mlsanity.com/paper/2311.18328/) (2023)
- [ABC-GS: Alignment-Based Controllable Style Transfer for 3D Gaussian Splatting](https://mlsanity.com/paper/2503.22218/) (2025)
- [LAENeRF: Local Appearance Editing for Neural Radiance Fields](https://mlsanity.com/paper/2312.09913/) (2023)
- [Learning Naturally Aggregated Appearance for Efficient 3D Editing](https://mlsanity.com/paper/2312.06657/) (2023)
- [NeRF-Insert: 3D Local Editing with Multimodal Control Signals](https://mlsanity.com/paper/2404.19204/) (2024)
- [Dream-in-Style: Text-to-3D Generation Using Stylized Score Distillation](https://mlsanity.com/paper/2406.18581/) (2024)
- [S-DyRF: Reference-Based Stylized Radiance Fields for Dynamic Scenes](https://mlsanity.com/paper/2403.06205/) (2024)
- [StyleCity: Large-Scale 3D Urban Scenes Stylization](https://mlsanity.com/paper/2404.10681/) (2024)
- [Improved 3D Scene Stylization via Text-Guided Generative Image Editing with Region-Based Control](https://mlsanity.com/paper/2509.05285/) (2025)
- [PQDAST: Depth-Aware Arbitrary Style Transfer for Games via Perceptual Quality-Guided Distillation](https://mlsanity.com/paper/2502.16996/) (2025)
- [SSGaussian: Semantic-Aware and Structure-Preserving 3D Style Transfer](https://mlsanity.com/paper/2509.04379/) (2025)
- [FROMAT: Multiview Material Appearance Transfer via Few-Shot Self-Attention Adaptation](https://mlsanity.com/paper/2512.09617/) (2025)
- [Image-Guided Geometric Stylization of 3D Meshes](https://mlsanity.com/paper/2604.07795/) (2026)
- [NeuMesh++: Towards Versatile and Efficient Volumetric Editing with Disentangled Neural Mesh-based Implicit Field](https://mlsanity.com/paper/2606.19316/) (2026)
- [OrnaStyler: Ornament-Aware Latent Editing for Content-Preserving 3D Stylization](https://mlsanity.com/paper/2608.29905/) (2026)

## Abstract (arXiv)

In recent years, there has been increasing interest in applying stylization on 3D scenes from a reference style image, in particular onto neural radiance fields (NeRF). While performing stylization directly on NeRF guarantees appearance consistency over arbitrary novel views, it is a challenging problem to guide the transfer of patterns from the style image onto different parts of the NeRF scene. In this work, we propose a stylization framework for NeRF based on local style transfer. In particular, we use a hash-grid encoding to learn the embedding of the appearance and geometry components, and show that the mapping defined by the hash table allows us to control the stylization to a certain extent. Stylization is then achieved by optimizing the appearance branch while keeping the geometry branch fixed. To support local style transfer, we propose a new loss function that utilizes a segmentation network and bipartite matching to establish region correspondences between the style image and the content images obtained from volume rendering. Our experiments show that our method yields plausible stylization results with novel view synthesis while having flexible controllability via manipulating and customizing the region correspondences.