DiffPano: Scalable and Consistent Text to Panorama Generation with Spherical Epipolar-Aware Diffusion
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arXiv
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| Format: | Preprint |
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2024
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| author | Ye, Weicai Ji, Chenhao Chen, Zheng Gao, Junyao Huang, Xiaoshui Zhang, Song-Hai Ouyang, Wanli He, Tong Zhao, Cairong Zhang, Guofeng |
| author_facet | Ye, Weicai Ji, Chenhao Chen, Zheng Gao, Junyao Huang, Xiaoshui Zhang, Song-Hai Ouyang, Wanli He, Tong Zhao, Cairong Zhang, Guofeng |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_24203 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | DiffPano: Scalable and Consistent Text to Panorama Generation with Spherical Epipolar-Aware Diffusion Ye, Weicai Ji, Chenhao Chen, Zheng Gao, Junyao Huang, Xiaoshui Zhang, Song-Hai Ouyang, Wanli He, Tong Zhao, Cairong Zhang, Guofeng Computer Vision and Pattern Recognition Artificial Intelligence Graphics Robotics 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. |
| title | DiffPano: Scalable and Consistent Text to Panorama Generation with Spherical Epipolar-Aware Diffusion |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Graphics Robotics |
| url | https://arxiv.org/abs/2410.24203 |