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:
尽管风格迁移技术在二维图像风格化方面已得到了充分发展，但这些方法在三维场景中的扩展仍相对较少被探索。现有方法在色彩和纹理迁移方面表现出色，但通常难以复制场景的几何结构。在我们的工作中，我们利用显式的高斯分布（Gaussian Splatting, GS）表示，通过使用地球移动者距离（Earth Mover's Distance, EMD）直接匹配风格和内容场景之间的高斯分布。通过采用熵正则化的Wasserstein-2距离，我们确保了变换过程中的空间平滑性。此外，我们将场景风格化问题分解为更小的部分，以提高效率。这一范式的转变将风格化从依赖潜在空间损失的生成过程重新定义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中的细节真实地迁移到内容场景中，实现了高分辨率的三维风格化。此外，WaSt-3D 在多种内容和风格场景中始终如一地交付结果，无需任何训练，因为它完全依赖于基于优化的技术。