mlsanityData to 7 Oct 2026

A Survey on Text-Driven 360-Degree Panorama Generation

arXiv 2502.14799 ↗First posted 20 Feb 2025cs.CV cs.AIcited by 0

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Abstract (arXiv)

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/.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 74 of its 112 references that are in the map. First posted on arXiv 20 Feb 2025.

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References · 74

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  1. 2025ViewPoint: Panoramic Video Generation with Pretrained Diffusion Models
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  3. 2025What Makes for Text to 360-degree Panorama Generation with Stable Diffusion?
  4. 2024Taming Stable Diffusion for Text to 360deg Panorama Image Generation
  5. 2026PSGS: Text-driven Panorama Sliding Scene Generation via Gaussian Splatting
  6. 2022HORIZON: High-Resolution Semantically Controlled Panorama Synthesis
  7. 2025Matrix-3D: Omnidirectional Explorable 3D World Generation
  8. 2025VideoPanda: Video Panoramic Diffusion with Multi-view Attention