ART3D: 面向文本引导艺术场景生成的3D高斯溅射技术
ART3D: 3D Gaussian Splatting for Text-Guided Artistic Scenes Generation
AI总结：
本研究提出结合扩散模型与3D高斯溅射的ART3D框架，通过图像语义迁移、深度一致性模块等技术生成高质量3D艺术场景，在内容与结构一致性指标上优于现有方法，推动AI艺术创作发展。
摘要：
本文通过提出ART3D这一结合扩散模型与3D高斯溅射技术的新型框架，探究了3D艺术场景生成领域现存的挑战。我们的方法通过一种创新的图像语义迁移算法，有效填补了艺术图像与真实图像之间的鸿沟。通过利用深度信息和初始艺术图像，我们生成点云图，解决了域差异问题。此外，我们提出了一种深度一致性模块，以增强3D场景的一致性。最后，生成的3D场景作为初始点用于优化高斯溅射。实验结果表明，与现有方法相比，ART3D在内容一致性和结构一致性指标上均表现更优。ART3D为生成高质量3D艺术场景提供了创新解决方案，显著推动了AI艺术创作领域的发展。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
