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 approach 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. Extensive experimental results demonstrate that our method ensures high-quality stylized scenes while offering significant advantages in style transfer speed and consistency.
我们提出了InstantStyleGaussian，这是一种基于3D高斯点绘（3D Gaussian Splatting，3DGS）场景表示的创新型3D风格迁移方法。通过输入目标风格图像，该方法能够快速生成新的3D GS场景。我们的方法在预先重建的GS场景上操作，结合了扩散模型和改进的迭代数据集更新策略。它利用扩散模型生成目标风格图像，将这些新图像添加到训练数据集中，并使用该数据集迭代更新和优化GS场景。广泛的实验结果表明，我们的方法在保证高质量风格化场景的同时，在风格迁移速度和一致性方面具有显著优势。