# BrushGaussian: Brushstroke-Based Stylization for 3D Gaussian Splatting
## 2 Related Works
### Two-Dimensional Image Style Transfer  
Image style transfer, also known as image stylization, aims to transform an input content image into a stylized version by extracting and recombining the artistic features of a reference style. The key challenge lies in balancing the preservation of semantic content with artistic expressiveness. A failed attempt may result in an image that is too photorealistic with minimal stylization or, conversely, one where the original content is completely lost. In the early stages, researchers designed hand-engineered filters to generate artistic effects, utilizing low-level operations such as blurring and sharpening [18; 19; 20; 21; 22]. These approaches provided fine-grained control over stylization and offered valuable insights into edge enhancement and texture manipulation. However, crafting effective filters was time-consuming and required expert knowledge [23], making it impractical for broader applications. More importantly, these methods struggled to capture the intricate and unique characteristics of an artist's style. To overcome these limitations, researchers began exploring example-based approaches, which automatically extract and apply artistic style features to arbitrary images. Figure 2: Overview of our method. We first prune pre-reconstructed 3D Gaussian Splatting (3 DCS) into a sparse 3 DGS and then apply texture mapping to project brushstroke references onto each Gaussian primitive. Once the stylization is complete, we achieve consistent, free-viewpoint stylized renderings. A significant breakthrough came with [PERSON] et al. [24], who first introduced a neural style transfer (NST) algorithm using a pre-trained convolutional neural network (VGG16) [25]. They discovered that a pre-trained VGG, originally designed for object recognition, could effectively separate image content from style, enabling its application to image stylization. This pioneering work marked the beginning of a new era in example-based style transfer, leading to a surge of follow-up research aimed at improving speed [26; 27; 28; 29; 30], enhancing visual quality [31; 32; 33; 34], ensuring stability for video stylization [35; 36], and introducing finer control over brushstrokes [37; 38; 39]. In general, these methods formulated style transfer as a feature matching problem, requiring a pre-trained neural network to extract and manipulate style features. An alternative perspective emerged, viewing image stylization as a domain adaptation problem, where the goal was to transform images from one distribution (content) to another (style). This shift in viewpoint led to the adoption of Generative Adversarial Networks (GANs) [40] for style transfer. [PERSON] et al. [41] introduced pix2 pix, a conditional GAN that generates stylized outputs based on paired training images. However, the requirement for well-aligned image pairs made training impractical for many real-world tasks. To address this, [PERSON] et al. [42] proposed CycleGAN, which introduced cycle consistency loss, enabling style transfer on unpaired datasets and significantly expanding the applicability of GAN-based stylization. Subsequent research extended GANs to multi-modal synthesis [43; 44], architectural improvements [45; 46; 47; 48; 49; 50], and style-conditioned generation [51; 52], further demonstrating their potential for artistic content creation. These advancements sparked widespread adoption, with numerous artists experimenting with GAN-based stylization, showcasing its power in image synthesis. Despite its success, GAN-based style transfer suffered from training instability and mode collapse, leading the research community to seek more robust alternatives. This paved the way for diffusion models [53; 54], which soon outperformed GANs in both stability and image quality. [PERSON] et al. [55] introduced latent space diffusion, significantly reducing computational costs while maintaining high-fidelity stylization.