Diffusion-based methods have achieved remarkable achievements in 2D image or 3D object generation, however, the generation of 3D scenes and even $360^{\circ}$ images remains constrained, due to the limited number of scene datasets, the complexity of 3D scenes themselves, and the difficulty of generating consistent multi-view images. To address these issues, we first establish a large-scale panoramic video-text dataset containing millions of consecutive panoramic keyframes with corresponding panoramic depths, camera poses, and text descriptions. Then, we propose a novel text-driven panoramic generation framework, termed DiffPano, to achieve scalable, consistent, and diverse panoramic scene generation. Specifically, benefiting from the powerful generative capabilities of stable diffusion, we fine-tune a single-view text-to-panorama diffusion model with LoRA on the established panoramic video-text dataset. We further design a spherical epipolar-aware multi-view diffusion model to ensure the multi-view consistency of the generated panoramic images. Extensive experiments demonstrate that DiffPano can generate scalable, consistent, and diverse panoramic images with given unseen text descriptions and camera poses.

本研究针对3D场景和360度图像生成面临的场景数据集不足、场景复杂性高和多视角一致性难题，提出了一种新的全景生成框架DiffPano。该框架通过建立大规模全景视频-文本数据集并利用稳定扩散的生成能力，精细调优了单视角文本到全景的扩散模型，从而实现可扩展且多样化的全景图像生成，实验结果表明其在给定的新文本描述和相机姿态下能够生成一致且多样的全景图像。

DiffPano：可扩展且一致的基于文本的全景生成方法