  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.
