Diffusion-based methods have achieved remarkable achievements in 2D image or 3D object generation, however, the generation of 3D scenes and even 360∘ 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.
@article{arxiv.2410.24203,
title = {DiffPano: Scalable and Consistent Text to Panorama Generation with Spherical Epipolar-Aware Diffusion},
author = {Weicai Ye and Chenhao Ji and Zheng Chen and Junyao Gao and Xiaoshui Huang and Song-Hai Zhang and Wanli Ouyang and Tong He and Cairong Zhao and Guofeng Zhang},
journal= {arXiv preprint arXiv:2410.24203},
year = {2024}
}