With the rapid development of XR, 3D generation and editing are becoming more and more important, among which, stylization is an important tool of 3D appearance editing. It can achieve consistent 3D artistic stylization given a single reference style image and thus is a user-friendly editing way. However, recent NeRF-based 3D stylization methods face efficiency issues that affect the actual user experience and the implicit nature limits its ability to transfer the geometric pattern styles. Additionally, the ability for artists to exert flexible control over stylized scenes is considered highly desirable, fostering an environment conducive to creative exploration. In this paper, we introduce StylizedGS, a 3D neural style transfer framework with adaptable control over perceptual factors based on 3D Gaussian Splatting (3DGS) representation. The 3DGS brings the benefits of high efficiency. We propose a GS filter to eliminate floaters in the reconstruction which affects the stylization effects before stylization. Then the nearest neighbor-based style loss is introduced to achieve stylization by fine-tuning the geometry and color parameters of 3DGS, while a depth preservation loss with other regularizations is proposed to prevent the tampering of geometry content. Moreover, facilitated by specially designed losses, StylizedGS enables users to control color, stylized scale and regions during the stylization to possess customized capabilities. Our method can attain high-quality stylization results characterized by faithful brushstrokes and geometric consistency with flexible controls. Extensive experiments across various scenes and styles demonstrate the effectiveness and efficiency of our method concerning both stylization quality and inference FPS.

本文介绍了StylizedGS，一种具有对感知因素可调控能力的3D神经风格迁移框架，基于3D高斯粒子喷洒(3DGS)表征。通过引入GS滤波器消除飘逸物，然后使用基于最近邻的风格损失来通过微调3DGS的几何和颜色参数来实现风格化，同时提出了一种具有其他正则化的深度保持损失来防止几何内容的篡改。此外，借助特别设计的损失函数，StylizedGS使用户可以在风格化过程中控制颜色、风格化比例和区域，具备定制化能力。广泛的实验证明了我们的方法在风格化质量和推理帧数方面的效果和效率。

StylizedGS: 可控的3D高斯描绘美化