A3GS: Arbitrary Artistic Style into Arbitrary 3D Gaussian Splatting

Recently, the field of 3D scene stylization has attracted considerable attention, particularly for applications in the metaverse. A key challenge is rapidly transferring the style of an arbitrary reference image to a 3D scene while faithfully preserving its content structure and spatial layout. Works leveraging implicit representations with gradient-based optimization achieve impressive style transfer results, yet the lengthy processing time per individual style makes rapid switching impractical. In this paper, we propose A 3 GS, a novel feed-forward neural network for zero-shot 3DGS stylization that enables transferring arbitrary image style to arbitrary 3DGS scenes in just 10 seconds without the need for per-style optimization. Our work introduces a Graph Convolutional Network (GCN) based autoencoder This ICCV paper is the Open Access version, provided by the Computer Vision Foundation.

Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore.

aimed at efficient feature aggregation and decoding of spatially structured 3DGS scenes. The encoder converts 3DGS scenes into a latent space. Furthermore, for the latent space, we utilize Adaptive Instance Normalization (AdaIN) to inject features from the target style image into the 3DGS scene. Finally, we constructed a 3DGS dataset using a generative model and proposed a two-stage training strategy for A 3 GS. Owing to the feed-forward design, our framework can perform fast style transfer on large-scale 3DGS scenes, which poses a severe challenge to the memory consumption of optimization-based methods. Extensive experiments demonstrate that our approach achieves high-quality, consistent 3D stylization in seconds.