Geometry-Aware Style Transfer in 3D Gaussian Splatting

In this paper, we present a novel geometry-aware style trans-fer framework for 3D Gaussian splatting (3DGS) that simultaneouslytransfers appearance attributes and geometric structures. Unlike priorworks that primarily focus on color-based stylization and often over-look structural adaptation, our method explicitly incorporates geometryadaptation through a decoupled optimization scheme that alternatelyupdates color and geometry parameters. This strategy alleviates poten-tial interference between color and geometry updates, leading to stableand consistent scene-level geometry transformation. The decoupled opti-mization is enabled by the proposed geometry-aware contrastive featurematching (GCFM). GCFM integrates RGB, depth, and edge cues intoa contrastive objective and is employed in both optimization phases toeffectively transfer structural characteristics from style images to Gaus-sian primitives. Extensive experiments show that our approach achievessuperior performance in both qualitative fidelity and quantitative met-rics, significantly outperforming existing 3DGS-based stylization meth-ods. Our code is available at https://github.com/oweixx/gast.