# BrushGaussian: Brushstroke-Based Stylization for 3D Gaussian Splatting
## 1 Introduction  
Style transfer aims to extract artistic style from a reference and apply it to a target object. This technique plays a crucial role in fields like computer-aided design (CAD), where it traditionally requires significant time and expertise to master both artistic skills and specialized software. For instance, mastering 3D modeling often requires at least six months of training in both artistic fundamentals (such as shape, color, and texture) and complex 3D software tools. However, with the advancement of style transfer techniques, individuals with minimal expertise can now generate artistic 3D content, significantly lowering the barrier to digital art creation. As a result, style transfer has become increasingly influential in various visual domains, including games, AR/VR, animation, and digital content creation. Style transfer can be categorized into two main branches: 2D and 3D style transfer. The 2D image-based approach typically takes a content image (providing semantic structure) and a style image (providing artistic features) as inputs and then synthesizes a stylized 2D image. However, unlike pixel-based representations in 2D images, 3D representations--such as meshes, point clouds, and volumetric data--vary significantly depending on the chosen primitive format. Extending 2D style transfer to 3D introduces several new constraints and challenges. One of the key challenges is choosing an appropriate 3D representation that ensures both accurate geometry and view consistency during styletransfer. Previous research has explored various 3D representations, including polygon meshes [1; 2; 3; 4; 5], point clouds [6; 7; 8; 9; 10; 11], and neural radiance fields (NeRFs) [12; 13; 14; 15; 16; 17]. However, each of these approaches comes with limitations: Meshes require well-defined polygonal connectivity; otherwise, the output may contain undesirable artifacts; point clouds may struggle with geometric accuracy or high memory consumption; NeRF-based approaches often suffer from slow rendering speeds, as they require intensive sampling through deep neural networks. More importantly, traditional 3D style transfer methods focus primarily on global features, such as texture and color, while neglecting local geometric attributes--such as brushstroke shapes. This contrasts with artistic intuition, as renowned painters, like [PERSON], are distinguished by their unique brushwork, which imparts a personal and expressive touch to their art. Capturing and transferring such geometric style elements remains an open challenge in 3D style transfer. To address these limitations, we propose enhancing 3D Gaussian primitives with texture features for efficient and expressive brushstroke-aware 3D style transfer. Our method transforms a photorealistic 3D Gaussian scene, reconstructed from multi-view images, into a stylized sparse 3D Gaussian scene that supports high-quality and view-consistent renderings from novel viewpoints (See Figure 1). Our approach (Figure 2) consists of three key components: (1) Gaussian Pruning for Efficiency: We first apply an unsupervised clustering algorithm to prune dense 3D Gaussians, improving both visibility and rendering efficiency. (2) Projected Area Computation: We compute the 2D projected area of each 3D Gaussian primitive in image space, providing a basis for texture mapping. (3) Texture Mapping for Stylization: We then project a style texture onto the computed Gaussian regions, ensuring that the brushstroke effects are accurately mapped onto the 3D scene. Compared to previous 3D stylization techniques, which often suffer from inaccurate geometry or a lack of fine-grained style details, our method provides structurally meaningful and visually appealing brushstroke-aware stylization. Figure 1: We propose BrushGS, a novel approach for stroke-based rendering. Our method begins with a pre-reconstructed Gaussian Splatting of a scene (**left**), which is then transformed into artistic Gaussian Splatting by applying texture mapping with a 2D brushstroke reference (**middle**).