A Survey on Text-Driven 360-Degree Panorama Generation

2025-02-20

AI Summary

This paper presents a systematic survey of text-to-360° panoramic image generation—a nascent task focused on synthesizing seamless spherical panoramas directly from natural language descriptions. We identify the fundamental tension between spherical geometric constraints and fine-grained text semantics as the core challenge. To address it, we propose a unified diffusion-based framework integrating spherical coordinate mapping, cross-view consistency regularization, and multimodal alignment. Our contribution includes: (1) the first standardized taxonomy covering algorithmic principles, performance limits, and application scenarios; (2) a clear articulation of key limitations—structural distortion, view inconsistency, and lack of 3D priors; and (3) a forward-looking roadmap toward embodied intelligence and 3D scene generation. Complementing the survey, we release an open-source resource page to enhance reproducibility and community standardization.

Technology Category

• Natural Language Processing: Generation
• Computer Vision: Computational Photography, Image & Video Synthesis
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies

Abstract

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 and their expanding applications in 360-degree 3D scene 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/.

Problem

• Text-driven 360-degree panorama generation
• Synthesis from textual descriptions
• Review of state-of-the-art algorithms

Innovation

• Text-driven panorama generation
• Text-to-image diffusion models
• 360-degree 3D scene synthesis