A Survey on Text-Driven 360-Degree Panorama Generation
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2025
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866918186634444800 |
|---|---|
| author | Wang, Hai Xiang, Xiaoyu Xia, Weihao Xue, Jing-Hao |
| author_facet | Wang, Hai Xiang, Xiaoyu Xia, Weihao Xue, Jing-Hao |
| contents | The advent of text-driven 360-degree panorama generation, enabling the synthesis of 360-degree panoramic images directly from textual descriptions, marks a transformative advancement in immersive visual content creation. This innovation significantly simplifies the traditionally complex process of producing such content. Recent progress in text-to-image diffusion models has accelerated the rapid development in this emerging field. This survey presents a comprehensive review of text-driven 360-degree panorama generation, offering an in-depth analysis of state-of-the-art algorithms. We extend our analysis to two closely related domains: text-driven 360-degree 3D scene generation and text-driven 360-degree panoramic video generation. Furthermore, we critically examine current limitations and propose promising directions for future research. A curated project page with relevant resources and research papers is available at https://littlewhitesea.github.io/Text-Driven-Pano-Gen/. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_14799 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Survey on Text-Driven 360-Degree Panorama Generation Wang, Hai Xiang, Xiaoyu Xia, Weihao Xue, Jing-Hao Computer Vision and Pattern Recognition Artificial Intelligence The advent of text-driven 360-degree panorama generation, enabling the synthesis of 360-degree panoramic images directly from textual descriptions, marks a transformative advancement in immersive visual content creation. This innovation significantly simplifies the traditionally complex process of producing such content. Recent progress in text-to-image diffusion models has accelerated the rapid development in this emerging field. This survey presents a comprehensive review of text-driven 360-degree panorama generation, offering an in-depth analysis of state-of-the-art algorithms. We extend our analysis to two closely related domains: text-driven 360-degree 3D scene generation and text-driven 360-degree panoramic video generation. Furthermore, we critically examine current limitations and propose promising directions for future research. A curated project page with relevant resources and research papers is available at https://littlewhitesea.github.io/Text-Driven-Pano-Gen/. |
| title | A Survey on Text-Driven 360-Degree Panorama Generation |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2502.14799 |