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.

翻译：本文通过引入ART3D这一融合扩散模型与三维高斯泼溅技术的新框架，探索了三维艺术场景生成中存在的现有挑战。我们的方法通过创新性的图像语义迁移算法，有效弥合了艺术图像与真实图像之间的差距。借助深度信息和初始艺术图像生成点云地图，解决了领域差异问题。此外，我们提出深度一致性模块以增强三维场景的连贯性。最终，三维场景作为优化高斯泼溅的初始点。实验结果表明，与现有方法相比，ART3D在内容与结构一致性指标上均表现出更优性能。ART3D通过为高质量三维艺术场景生成提供创新方案，显著推动了人工智能在艺术创作领域的发展。