While style transfer techniques have been well-developed for 2D image stylization, the extension of these methods to 3D scenes remains relatively unexplored. Existing approaches demonstrate proficiency in transferring colors and textures but often struggle with replicating the geometry of the scenes. In our work, we leverage an explicit Gaussian Splatting (GS) representation and directly match the distributions of Gaussians between style and content scenes using the Earth Mover's Distance (EMD). By employing the entropy-regularized Wasserstein-2 distance, we ensure that the transformation maintains spatial smoothness. Additionally, we decompose the scene stylization problem into smaller chunks to enhance efficiency. This paradigm shift reframes stylization from a pure generative process driven by latent space losses to an explicit matching of distributions between two Gaussian representations. Our method achieves high-resolution 3D stylization by faithfully transferring details from 3D style scenes onto the content scene. Furthermore, WaSt-3D consistently delivers results across diverse content and style scenes without necessitating any training, as it relies solely on optimization-based techniques. See our project page for additional results and source code: $\href{https://compvis.github.io/wast3d/}{https://compvis.github.io/wast3d/}$.

翻译：尽管风格迁移技术在二维图像风格化领域已发展得相当成熟，但将这些方法扩展到三维场景的研究仍相对较少。现有方法在色彩与纹理迁移方面表现出色，但在复现场景几何结构方面往往存在困难。在本工作中，我们利用显式的高斯泼溅表示，并直接使用推土机距离匹配风格场景与内容场景间的高斯分布。通过采用熵正则化的Wasserstein-2距离，我们确保变换过程保持空间平滑性。此外，我们将场景风格化问题分解为更小的模块以提升计算效率。这种范式转变将风格化从纯粹由隐空间损失驱动的生成过程，重构为两个高斯表示之间的显式分布匹配。我们的方法通过将三维风格场景的细节忠实迁移到内容场景上，实现了高分辨率的三维风格化。更重要的是，WaSt-3D完全依赖基于优化的技术，无需任何训练即可在不同内容与风格场景间保持稳定的输出效果。更多结果与源代码请参见项目页面：$\href{https://compvis.github.io/wast3d/}{https://compvis.github.io/wast3d/}$。