Multi-StyleGS: Stylizing Gaussian Splatting with Multiple Styles (2506.06846v1)

Abstract: In recent years, there has been a growing demand to stylize a given 3D scene to align with the artistic style of reference images for creative purposes. While 3D Gaussian Splatting(GS) has emerged as a promising and efficient method for realistic 3D scene modeling, there remains a challenge in adapting it to stylize 3D GS to match with multiple styles through automatic local style transfer or manual designation, while maintaining memory efficiency for stylization training. In this paper, we introduce a novel 3D GS stylization solution termed Multi-StyleGS to tackle these challenges. In particular, we employ a bipartite matching mechanism to au tomatically identify correspondences between the style images and the local regions of the rendered images. To facilitate local style transfer, we introduce a novel semantic style loss function that employs a segmentation network to apply distinct styles to various objects of the scene and propose a local-global feature matching to enhance the multi-view consistency. Furthermore, this technique can achieve memory efficient training, more texture details and better color match. To better assign a robust semantic label to each Gaussian, we propose several techniques to regularize the segmentation network. As demonstrated by our comprehensive experiments, our approach outperforms existing ones in producing plausible stylization results and offering flexible editing.

• The paper introduces Multi-StyleGS, which integrates Gaussian Splatting with a bipartite matching mechanism for localized style transfer in 3D scenes.
• It employs a semantic style loss function combined with local-global feature matching to ensure multi-view consistency and enhanced texture details.
• The framework reduces memory usage by partitioning scenes into local regions, outperforming existing methods in visual quality and editability.

Interpreting Multi-StyleGS: Stylizing Gaussian Splatting with Multiple Styles

The paper "Multi-StyleGS: Stylizing Gaussian Splatting with Multiple Styles" presents an advanced methodology for stylizing 3D scenes using style transfer mechanisms rooted in Gaussian Splatting (GS). Researchers Yangkai Lin, Jiabao Lei, and Kui Jia have focused on addressing the inherent challenges of implementing multiple styles concurrently within 3D scene stylization, while also striving to enhance memory efficiency during training procedures.

Overview of Key Contributions

The authors introduce Multi-StyleGS, a novel stylization framework leveraging 3D GS as a base representation to facilitate real-time rendering and explicit characteristic manipulation. A primary highlight of this work is the incorporation of a bipartite matching mechanism, enabling automatic establishment of correspondences between regions in style images and local areas in 3D scenes. By doing so, the approach empowers localized style transfer while ensuring multi-view consistency—an often arduous task in 3D content styling.

Key technical contributions featured in the paper include:

1. Semantic Style Loss Function: Integral to the proposed local style transfer workflow, this function utilizes a segmentation network to apply varied styles across disparate objects within a scene. The segmentation network is regularized using techniques like Gaussian smoothing and semantic importance filtering, maintaining robust semantic labels for each Gaussian.
2. Local-Global Feature Matching: By combining local VGG features with DINOv2 global features, the authors address the problem of multi-view inconsistency. This combination enhances texture details and color match while maintaining consistency across different viewing angles.
3. Memory Efficient Training Methodology: Multi-StyleGS circumvents typical memory bottlenecks by partitioning scenes into distinct local regions for independent optimization, significantly reducing the memory footprint.

Numerical Results and Claims

Experimental evaluations, conducted on diverse datasets like Tanks and Temples and LLFF, demonstrate that Multi-StyleGS exceeds existing methods in producing visually plausible, stylistically consistent, and editable stylized images. For instance, single-image Frechet Inception Distance (SIFID) measurements affirm the quality of stylistic similarity achieved by the model. Moreover, multi-view consistency evaluations display lower scores for Multi-StyleGS relative to competitors like ARF, SNeRF, and LSNeRF, indicating superior consistency across rendered perspectives.

Implications and Future Directions

The work presents a robust approach to 3D scene style transfer, with potential applications in creative industries needing realistic yet customizable scene representations. Possible future developments involve enhancing the procedural efficiency to accommodate real-time style transfer capabilities, as the current methodology requires extensive training for different stylistic adaptations. Additionally, the exploration of integrating other neural networks for more dynamic scene stylizations could provide fruitful avenues for research.

In conclusion, Multi-StyleGS has introduced a significant advancement towards achieving memory-efficient, stylistically diverse 3D scene representations using Gaussian Splatting. The methodology integrates semantic segmentation and innovative feature matching, setting a new benchmark in the precision and editability of stylizing artistic 3D content.