综述文本生成全景图技术，涵盖算法、应用与未来方向
A Survey on Text-Driven 360-Degree Panorama Generation
- 系统梳理文本驱动全景图生成的主流算法
- 覆盖全景图、3D场景及视频生成三类相关任务
- 适合关注生成式视觉与沉浸式内容的研究者
文本驱动的360度全景图生成技术，可直接根据文本描述合成全景图像，显著简化了沉浸式视觉内容创作流程。近年来，文本到图像的扩散模型发展迅速，推动了该领域的快速进步。本文对文本驱动360度全景图生成进行了全面综述，深入分析当前先进算法，并拓展至两个密切相关领域：文本驱动的360度3D场景生成和文本驱动的360度全景视频生成。此外，本文还批判性地探讨了现有局限，并提出未来研究的潜在方向。相关资源与论文列表可访问项目主页：https://littlewhitesea.github.io/Text-Driven-Pano-Gen/
原文摘要 · Abstract (English)
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/.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。