A Survey on Text-Driven 360-Degree Panorama Generation
It addresses the problem of simplifying immersive visual content creation for researchers and practitioners, but is incremental as a survey.
This survey reviews text-driven 360-degree panorama generation, analyzing state-of-the-art algorithms and extending to related domains like 3D scene and video generation, while identifying limitations and future directions.
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/.