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.
在本文中，我们通过引入ART3D这一新型框架，探讨了3D艺术场景生成中存在的挑战，该框架结合了扩散模型和3D高斯喷涂技术。我们的方法通过一个创新的图像语义转移算法，有效地弥合了艺术图像与现实图像之间的差距。通过利用深度信息和初始艺术图像，我们生成了一个点云图，解决了领域差异问题。此外，我们提出了一个深度一致性模块，以增强3D场景的一致性。最后，3D场景作为优化高斯喷点的初始点。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表现出优越的性能。ART3D通过为生成高质量3D艺术场景提供创新解决方案，显著推进了AI在艺术创作领域的发展。