# StyleSplat: 3D Object Style Transfer with Gaussian Splatting

arXiv: https://arxiv.org/abs/2407.09473 · HTML: https://mlsanity.com/paper/2407.09473/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 39 references that are in the map.
- First posted on arXiv 12 Jul 2024.

## Builds on (most cited first, 28 of 28)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Emerging Properties in Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.14294/) (2021)
- [3D Gaussian Splatting for Real-Time Radiance Field Rendering](https://mlsanity.com/paper/2308.04079/) (2023)
- [Instant Neural Graphics Primitives with a Multiresolution Hash Encoding](https://mlsanity.com/paper/2201.05989/) (2022)
- [Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection](https://mlsanity.com/paper/2303.05499/) (2023)
- [Perceptual Losses for Real-Time Style Transfer and Super-Resolution](https://mlsanity.com/paper/1603.08155/) (2016)
- [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)
- [Plenoxels: Radiance Fields without Neural Networks](https://mlsanity.com/paper/2112.05131/) (2021)
- [A Neural Algorithm of Artistic Style](https://mlsanity.com/paper/1508.06576/) (2015)
- [Gaussian Grouping: Segment and Edit Anything in 3D Scenes](https://mlsanity.com/paper/2312.00732/) (2023)
- [Feature 3DGS: Supercharging 3D Gaussian Splatting to Enable Distilled Feature Fields](https://mlsanity.com/paper/2312.03203/) (2023)
- [GaussianEditor: Swift and Controllable 3D Editing with Gaussian Splatting](https://mlsanity.com/paper/2311.14521/) (2023)
- [Differentiable Surface Splatting for Point-based Geometry Processing](https://mlsanity.com/paper/1906.04173/) (2019)
- [Tracking Anything with Decoupled Video Segmentation](https://mlsanity.com/paper/2309.03903/) (2023)
- [Style Transfer by Relaxed Optimal Transport and Self-Similarity](https://mlsanity.com/paper/1904.12785/) (2019)
- [Visual Attribute Transfer through Deep Image Analogy](https://mlsanity.com/paper/1705.01088/) (2017)
- [StyleGaussian: Instant 3D Style Transfer with Gaussian Splatting](https://mlsanity.com/paper/2403.07807/) (2024)
- [TIP-Editor: An Accurate 3D Editor Following Both Text-Prompts And Image-Prompts](https://mlsanity.com/paper/2401.14828/) (2024)
- [Arbitrary Style Transfer with Deep Feature Reshuffle](https://mlsanity.com/paper/1805.04103/) (2018)
- [Stroke Controllable Fast Style Transfer with Adaptive Receptive Fields](https://mlsanity.com/paper/1802.07101/) (2018)
- [StylizedGS: Controllable Stylization for 3D Gaussian Splatting](https://mlsanity.com/paper/2404.05220/) (2024)
- [Gaussian Splatting in Style](https://mlsanity.com/paper/2403.08498/) (2024)
- [CoARF: Controllable 3D Artistic Style Transfer for Radiance Fields](https://mlsanity.com/paper/2404.14967/) (2024)
- [ICE-G: Image Conditional Editing of 3D Gaussian Splats](https://mlsanity.com/paper/2406.08488/) (2024)
- [Son of Zorn's Lemma: Targeted Style Transfer Using Instance-aware Semantic Segmentation](https://mlsanity.com/paper/1701.02357/) (2017)
- [Class-Based Styling: Real-time Localized Style Transfer with Semantic Segmentation](https://mlsanity.com/paper/1908.11525/) (2019)
- [ARF-Plus: Controlling Perceptual Factors in Artistic Radiance Fields for 3D Scene Stylization](https://mlsanity.com/paper/2308.12452/) (2023)

## Built on by (most cited first, 18 of 18)

- [A Survey on 3D Gaussian Splatting Applications: Segmentation, Editing, and Generation](https://mlsanity.com/paper/2508.09977/) (2025)
- [DecoupledGaussian: Object-Scene Decoupling for Physics-Based Interaction](https://mlsanity.com/paper/2503.05484/) (2025)
- [TexGS-VolVis: Expressive Scene Editing for Volume Visualization via Textured Gaussian Splatting](https://mlsanity.com/paper/2507.13586/) (2025)
- [TRASE: Tracking-free 4D Segmentation and Editing](https://mlsanity.com/paper/2411.19290/) (2024)
- [CLIPGaussian: Universal and Multimodal Style Transfer Based on Gaussian Splatting](https://mlsanity.com/paper/2505.22854/) (2025)
- [3DOT: Texture Transfer for 3DGS Objects from a Single Reference Image](https://mlsanity.com/paper/2503.18853/) (2025)
- [ArtNVG: Content-Style Separated Artistic Neighboring-View Gaussian Stylization](https://mlsanity.com/paper/2412.18783/) (2024)
- [FantasyStyle: Controllable Stylized Distillation for 3D Gaussian Splatting](https://mlsanity.com/paper/2508.08136/) (2025)
- [Tune-Your-Style: Intensity-tunable 3D Style Transfer with Gaussian Splatting](https://mlsanity.com/paper/2602.00618/) (2026)
- [Editing Implicit and Explicit Representations of Radiance Fields: A Survey](https://mlsanity.com/paper/2412.17628/) (2024)
- [Mastering Regional 3DGS: Locating, Initializing, and Editing with Diverse 2D Priors](https://mlsanity.com/paper/2507.05426/) (2025)
- [Optimization-Free Style Transfer for 3D Gaussian Splats](https://mlsanity.com/paper/2508.05813/) (2025)
- [Image-Conditioned 3D Gaussian Splat Quantization](https://mlsanity.com/paper/2508.15372/) (2025)
- [DiffStyle3D: Consistent 3D Gaussian Stylization via Attention Optimization](https://mlsanity.com/paper/2601.19717/) (2026)
- [Structured 3D Latents Are Surprisingly Powerful: Unleashing Generalizable Style with 2D Diffusion](https://mlsanity.com/paper/2605.04412/) (2026)
- [Boosting Zero-Shot 3D Style Transfer with 2D Pre-trained Priors](https://mlsanity.com/paper/2605.30065/) (2026)
- [OmniStyle-INR: Universal and Multimodal Style Transfer for INRs](https://mlsanity.com/paper/2607.16362/) (2026)
- [Owner3D: Ownership-Guided Style Writing for Training-Free Localized 3D Stylization](https://mlsanity.com/paper/2608.14078/) (2026)

## Abstract (arXiv)

Recent advancements in radiance fields have opened new avenues for creating high-quality 3D assets and scenes. Style transfer can enhance these 3D assets with diverse artistic styles, transforming creative expression. However, existing techniques are often slow or unable to localize style transfer to specific objects. We introduce StyleSplat, a lightweight method for stylizing 3D objects in scenes represented by 3D Gaussians from reference style images. Our approach first learns a photorealistic representation of the scene using 3D Gaussian splatting while jointly segmenting individual 3D objects. We then use a nearest-neighbor feature matching loss to finetune the Gaussians of the selected objects, aligning their spherical harmonic coefficients with the style image to ensure consistency and visual appeal. StyleSplat allows for quick, customizable style transfer and localized stylization of multiple objects within a scene, each with a different style. We demonstrate its effectiveness across various 3D scenes and styles, showcasing enhanced control and customization in 3D creation.