Geometry-Aware Style Transfer in 3D Gaussian Splatting

2026-06-23

AI Summary

Existing 3D Gaussian splatting-based style transfer methods are largely confined to color stylization and often neglect geometric adaptation, leading to inconsistencies in the overall scene structure. This work proposes a geometry-aware joint transfer framework that, for the first time, simultaneously optimizes appearance and geometric features within 3D Gaussian splatting. The approach employs a decoupled optimization strategy that alternately updates color and geometry parameters, complemented by a Geometry-aware Contrastive Feature Matching (GCFM) mechanism that integrates RGB, depth, and edge information for contrastive learning. By explicitly aligning geometric structure with visual style, the method effectively mitigates interference between color and geometry updates. Extensive experiments demonstrate that our approach significantly outperforms existing techniques both qualitatively and quantitatively, achieving high-fidelity style transfer with consistent 3D structural integrity.

Abstract

In this paper, we present a novel geometry-aware style transfer framework for 3D Gaussian splatting (3DGS) that simultaneously transfers appearance attributes and geometric structures. Unlike prior works that primarily focus on color-based stylization and often overlook structural adaptation, our method explicitly incorporates geometry adaptation through a decoupled optimization scheme that alternately updates color and geometry parameters. This strategy alleviates potential interference between color and geometry updates, leading to stable and consistent scene-level geometry transformation. The decoupled optimization is enabled by the proposed geometry-aware contrastive feature matching (GCFM). GCFM integrates RGB, depth, and edge cues into a contrastive objective and is employed in both optimization phases to effectively transfer structural characteristics from style images to Gaussian primitives. Extensive experiments show that our approach achieves superior performance in both qualitative fidelity and quantitative metrics, significantly outperforming existing 3DGS-based stylization methods. Our code is available at \href{https://github.com/oweixx/gast}{https://github.com/oweixx/gast}.

Problem

• style transfer
• 3D Gaussian splatting
• geometry adaptation
• structural stylization

Innovation

• Geometry-Aware Style Transfer
• 3D Gaussian Splatting
• Decoupled Optimization
• Contrastive Feature Matching
• Geometric Adaptation