# BrushGaussian: Brushstroke-Based Stylization for 3D Gaussian Splatting
## 4 Experiments
### Comparison with Other Methods  
We provide a visual comparison with state-of-the-art 3D style transfer methods [13; 14]. Specifically, [PERSON] et al. [13] introduce a nearest-neighbor-based loss, which is highly effective in capturing style details while maintaining multi-view consistency, replacing the traditional Gram matrix-based VGG loss. [PERSON] et al. [14] propose StyleRF, which restores high-fidelity 3D geometry and enables zero-shot style transfer through a reference-style feature grid. It introduces two innovations: sampling-invariant content transformation for multi-view consistency and deferred style transformation to reduce memory usage. For both methods, we use the released code and train the models from scratch. We choose to carry out comparisons with image reference-based 3D stylization methods rather than text-based methods [16; 75], as they offer a more intuitive and fair comparison for our brushstroke-based approach.  
Visually, we observe that our results provide a more accurate brushstroke shape (Figure 7) representation based on the provided image reference compared to the baselines. For example, in the chair scene shown in Figure 8, we use a brushstroke reference that features a \"splatting\" style. Our method not only preserves this splatter-like appearance, creating a truly \"Gaussian Splatting\", but also accurately converts the object's shape, demonstrating both geometric fidelity and artistic expressiveness. In contrast, the baseline methods of ARF [13] and StyleRF [14] leave the pre-reconstructed density field unchanged, focusing only on stylizing surface color and texture. As a result, their outputs appear less vivid.  
To show that our method transfers brush-stroke style without sacrificing scene semantics, we conduct a qualitative study with CLIP [78]. For every stylized rendering, we compute the cosine similarity between its CLIP image embedding and the text prompt <This is an image of #->, where # is replaced by the NeRF-scene label (chair, ficus, hotdog, lego, materials, and mic). Table 1 lists the CLIP similarity scores for our approach and the baselines under several brush-stroke styles. Higher values indicate the better preservation of the underlying semantic content. As shown in Table 1, our method attains the highest average CLIP similarity among all compared methods, demonstrating its effectiveness in maintaining the original scene's semantics during 3D style transfer.  
Figure 6: Results with varying numbers of brushstrokes. By increasing the number of strokes, our stroke-based rendering captures more details and enhances the overall artistic effect.  
Figure 8: Comparison with baseline methods ARF [13] and StyleRF [14] on the NeRF Synthetic dataset. Our stylized novel views faithfully capture the brushstroke features from the provided reference, yielding vivid stroke-based details. In contrast, the baseline methods focus primarily on color and texture, resulting in less vibrant and representative outputs.  
Figure 7: Local-detail comparison with ARF [13] and StyleRF [14]. Our method faithfully captures the characteristic shape and texture of the reference brushstroke, yielding stylizations that are more consistent with artistic intuition and visually pleasing than the baseline approaches.