SphereDiff: Tuning-free Omnidirectional Panoramic Image and Video
  Generation via Spherical Latent Representation
Authors: name: Minho Park, name: Taewoong Kang, name: Jooyeol Yun, name: Sungwon Hwang, name: Jaegul Choo
Organization: KAIST AI

The increasing demand for AR/VR applications has highlighted the need for
high-quality 360-degree panoramic content. However, generating high-quality
360-degree panoramic images and videos 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 attempt tuning-free methods that still rely on ERP latent representations,
leading to discontinuities near the poles. In this paper, we introduce
SphereDiff, a novel approach for seamless 360-degree panoramic image and video
generation using state-of-the-art diffusion models without additional tuning.
We define a spherical latent representation that ensures uniform distribution
across all perspectives, mitigating the distortions inherent in ERP. We extend
MultiDiffusion to spherical latent space and propose a 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 in the projection process. Our method outperforms existing
approaches in generating 360-degree panoramic content while maintaining high
fidelity, making it a robust solution for immersive AR/VR applications. The
code is available here. https://github.com/pmh9960/SphereDiff