G-Style: Stylized Gaussian Splatting
Not on the map. Why?
Abstract (arXiv)
We introduce G-Style, a novel algorithm designed to transfer the style of an image onto a 3D scene represented using Gaussian Splatting. Gaussian Splatting is a powerful 3D representation for novel view synthesis, as -- compared to other approaches based on Neural Radiance Fields -- it provides fast scene renderings and user control over the scene. Recent pre-prints have demonstrated that the style of Gaussian Splatting scenes can be modified using an image exemplar. However, since the scene geometry remains fixed during the stylization process, current solutions fall short of producing satisfactory results. Our algorithm aims to address these limitations by following a three-step process: In a pre-processing step, we remove undesirable Gaussians with large projection areas or highly elongated shapes. Subsequently, we combine several losses carefully designed to preserve different scales of the style in the image, while maintaining as much as possible the integrity of the original scene content. During the stylization process and following the original design of Gaussian Splatting, we split Gaussians where additional detail is necessary within our scene by tracking the gradient of the stylized color. Our experiments demonstrate that G-Style generates high-quality stylizations within just a few minutes, outperforming existing methods both qualitatively and quantitatively.
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 34 of its 47 references that are in the map. First posted on arXiv 28 Aug 2024.
Builds on
34 of 47 references.
References · 34
- 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
- 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,364
- 20233D Gaussian Splatting for Real-Time Radiance Field Renderingcited by 6,226
- 2017Arbitrary Style Transfer in Real-time with Adaptive Instance Normalizationcited by 2,010
- 2021Plenoxels: Radiance Fields without Neural Networkscited by 1,613
- 2019Local Light Field Fusion: Practical View Synthesis with Prescriptive Sampling Guidelinescited by 672
- 2015A Neural Algorithm of Artistic Stylecited by 483
- 2022Inversion-Based Style Transfer with Diffusion Modelscited by 291
- 2024A Survey on 3D Gaussian Splattingcited by 224
- 2023Style Injection in Diffusion: A Training-free Approach for Adapting Large-scale Diffusion Models for Style Transfercited by 196
- 2019Style Transfer by Relaxed Optimal Transport and Self-Similaritycited by 168
- 2023StyleDiffusion: Controllable Disentangled Style Transfer via Diffusion Modelscited by 155
22 more
- 2022ARF: Artistic Radiance Fieldscited by 147
- 2022StylizedNeRF: Consistent 3D Scene Stylization as Stylized NeRF via 2D-3D Mutual Learningcited by 140
- 2024Recent Advances in 3D Gaussian Splattingcited by 131
- 2017Visual Attribute Transfer through Deep Image Analogycited by 123
- 20243D Gaussian as a New Era: A Surveycited by 104
- 2016Combining Markov Random Fields and Convolutional Neural Networks for Image Synthesiscited by 102
- 2016Fast Patch-based Style Transfer of Arbitrary Stylecited by 92
- 2023StyleRF: Zero-shot 3D Style Transfer of Neural Radiance Fieldscited by 79
- 2024StyleGaussian: Instant 3D Style Transfer with Gaussian Splattingcited by 79
- 2021Learning to Stylize Novel Viewscited by 76
- 2018Arbitrary Style Transfer with Deep Feature Reshufflecited by 75
- 2021StyleMesh: Style Transfer for Indoor 3D Scene Reconstructionscited by 58
- 2019Learning a Neural 3D Texture Space from 2D Exemplarscited by 53
- 20213D Photo Stylization: Learning to Generate Stylized Novel Views from a Single Imagecited by 45
- 2022SNeRF: Stylized Neural Implicit Representations for 3D Scenescited by 26
- 2024StylizedGS: Controllable Stylization for 3D Gaussian Splattingcited by 23
- 2024Gaussian Splatting in Stylecited by 16
- 2024CoARF: Controllable 3D Artistic Style Transfer for Radiance Fieldscited by 14
- 2020On Demand Solid Texture Synthesis Using Deep 3D Networkscited by 7
- 2024Surface-aware Mesh Texture Synthesis with Pre-trained 2D CNNscited by 3
- 2021STS-GAN: Can We Synthesize Solid Texture with High Fidelity from Arbitrary 2D Exemplar?cited by 2
- 2023Universal Photorealistic Style Transfer: A Lightweight and Adaptive Approachcited by 2
Built on by
Citing papers per year: 2024: 1; 2025: 3; 2026: 1.
5 papers.
- 2024SGSST: Scaling Gaussian Splatting StyleTransfercited by 20
- 20253D Stylization via Large Reconstruction Modelcited by 8
- 2025ABC-GS: Alignment-Based Controllable Style Transfer for 3D Gaussian Splattingcited by 7
- 2025Optimization-Free Style Transfer for 3D Gaussian Splatscited by 0
- 2026Boosting Zero-Shot 3D Style Transfer with 2D Pre-trained Priorscited by 0
- 2026Boosting Zero-Shot 3D Style Transfer with 2D Pre-trained Priors28 May 2026
- 2025Optimization-Free Style Transfer for 3D Gaussian Splats7 Aug 2025
- 20253D Stylization via Large Reconstruction Model30 Apr 2025
- 2025ABC-GS: Alignment-Based Controllable Style Transfer for 3D Gaussian Splatting28 Mar 2025
- 2024SGSST: Scaling Gaussian Splatting StyleTransfer4 Dec 2024