# SphereDiff: Tuning-free 360deg Static and Dynamic Panorama Generation via Spherical Latent Representation

arXiv: https://arxiv.org/abs/2504.14396 · HTML: https://mlsanity.com/paper/2504.14396/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in 3D, point cloud and scene generation, one of 14 directions in Diffusion models.
- First posted on arXiv 19 Apr 2025.

## Placed in

- Diffusion models / [3D, point cloud and scene generation](https://mlsanity.com/diffusion/3d-point-cloud-and-scene-generation/)

## Built on by (most cited first, 2 of 2)

- [AlayaVista: Streaming World Modeling from Panoramic States to Perspective Video](https://mlsanity.com/paper/2609.14462/) (2026)
- [HyperbolicDiffusion: Sharp & Scalable Tiled Generation on the Hyperbolic Plane](https://mlsanity.com/paper/2608.03422/) (2026)

## Abstract (arXiv)

The increasing demand for AR/VR applications has highlighted the need for high-quality content, such as 360deg live wallpapers. However, generating high-quality 360deg panoramic contents remains a challenging task due to the severe distortions introduced by equirectangular projection (ERP). Existing approaches either fine-tune pretrained diffusion models on limited ERP datasets or adopt tuning-free methods that still rely on ERP latent representations, often resulting in distracting distortions near the poles. In this paper, we introduce SphereDiff, a novel approach for synthesizing 360deg static and live wallpaper with state-of-the-art diffusion models without additional tuning. We define a spherical latent representation that ensures consistent quality across all perspectives, including near the poles. Then, we extend MultiDiffusion to spherical latent representation and propose a dynamic spherical latent sampling method to enable direct use of pretrained diffusion models. Moreover, we introduce distortion-aware weighted averaging to further improve the generation quality. Our method outperforms existing approaches in generating 360deg static and live wallpaper, making it a robust solution for immersive AR/VR applications. The code is available here. https://github.com/pmh9960/SphereDiff