In this paper, we explore the existing challenges in 3D artistic scene
generation by introducing ART3D, a novel framework that combines diffusion
models and 3D Gaussian splatting techniques. Our method effectively bridges the
gap between artistic and realistic images through an innovative image semantic
transfer algorithm. By leveraging depth information and an initial artistic
image, we generate a point cloud map, addressing domain differences.
Additionally, we propose a depth consistency module to enhance 3D scene
consistency. Finally, the 3D scene serves as initial points for optimizing
Gaussian splats. Experimental results demonstrate ART3D's superior performance
in both content and structural consistency metrics when compared to existing
methods. ART3D significantly advances the field of AI in art creation by
providing an innovative solution for generating high-quality 3D artistic
scenes.

通过结合扩散模型和 3D 高斯喷洒技术提出一种名为 ART3D 的新型框架，有效地通过创新的图像语义迁移算法来弥合艺术与现实图像之间的差距，并且通过利用深度信息和初始艺术图像生成点云地图来解决领域差异，并且提出了深度一致性模块来提高 3D 场景的一致性，最后，3D 场景作为优化高斯喷洒的初始点，实验结果表明与现有方法相比，ART3D 在内容和结构一致性指标上具有优越的性能，为生成高质量的 3D 艺术场景提供了创新解决方案，极大地推动了人工智能在艺术创作领域的发展。