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.
我们介绍了G-Style，这是一种新颖的算法，用于将图像的风格转移到使用高斯斑点表示的3D场景中。高斯斑点是一种强大的3D表示方法，特别适用于新视角合成，因为与基于神经辐射场的其他方法相比，它提供了快速的场景渲染和对场景的用户控制。近期的预印本已展示了如何使用图像样本修改高斯斑点场景的风格。然而，由于场景几何在风格化过程中保持不变，现有解决方案未能产生令人满意的结果。 我们的算法旨在通过以下三步过程来解决这些局限性：在预处理步骤中，我们去除投影面积较大或形状高度延伸的非理想高斯。随后，我们结合了几种精心设计的损失函数，以保留图像中不同尺度的风格，同时尽可能保持原始场景内容的完整性。在风格化过程中，按照高斯斑点的原始设计，我们根据风格化颜色的梯度跟踪，在场景中拆分需要额外细节的高斯。我们的实验表明，G-Style能够在短短几分钟内生成高质量的风格化效果，并在定性和定量方面超越了现有方法。