InstantStyleGaussian: Efficient Art Style Transfer with 3D Gaussian Splatting

作者：Xinyi Yu, Jun Yu, Libo Zhou, Yan Wei, Linlin Ou · 发表于：arXiv.org · 年份：2024 · DOI：10.48550/arxiv.2408.04249 · 被引用次数：14 · 研究领域：Computer Science

We present InstantStyleGaussian, an innovative 3D style transfer method based on the 3D Gaussian Splatting (3DGS) scene representation. By inputting a target-style image, it quickly generates new 3D GS scenes. Our method operates on pre-reconstructed GS scenes, combining diffusion models with an improved iterative dataset update strategy. It utilizes diffusion models to generate target style images, adds these new images to the training dataset, and uses this dataset to iteratively update and optimize the GS scenes, significantly accelerating the style editing process while ensuring the quality of the generated scenes. Extensive experimental results demonstrate that our method ensures high-quality stylized scenes while offering significant advantages in style transfer speed and consistency.