title: SphereDiff: Tuning-free Omnidirectional Panoramic Image and Video
    Generation via Spherical Latent Representation
summary: 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
paper_authors._id: 6806fcc4f349e60f6c1b928c
paper_authors.name: Minho Park
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-04-22T09:50:59.880Z
paper_authors._id: 630461624ec2dfa82a5ad7e7
paper_authors.avatarUrl: /avatars/6696e21069494552b81a28a899a28fd1.svg
paper_authors.fullname: Minho Park
paper_authors.type: user
paper_authors.user: mpark
paper_authors._id: 6806fcc4f349e60f6c1b928d
paper_authors.name: Taewoong Kang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-04-22T10:52:46.869Z
paper_authors._id: 679ecfd537a46d35e42f068d
paper_authors.avatarUrl: /avatars/d3e891401105b5ef7196cb057ad92be0.svg
paper_authors.fullname: TaewoongKang
paper_authors.type: user
paper_authors.user: TaewoongKang
paper_authors._id: 6806fcc4f349e60f6c1b928e
paper_authors.name: Jooyeol Yun
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-04-22T09:51:02.918Z
paper_authors._id: 6369f693bf21b20c5692937b
paper_authors.avatarUrl: /avatars/e937dc8234b3e456149882bfce34841f.svg
paper_authors.fullname: Jooyeol Yun
paper_authors.type: user
paper_authors.user: YeolJoo
paper_authors._id: 6806fcc4f349e60f6c1b928f
paper_authors.name: Sungwon Hwang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-04-22T10:52:52.332Z
paper_authors._id: 642fcfc0a043f0ac7deeaae0
paper_authors.avatarUrl: /avatars/6cc46dd480cdc0d86c7a509e22782a13.svg
paper_authors.fullname: Sungwon Hwang
paper_authors.type: user
paper_authors.user: sungwon95
paper_authors._id: 6806fcc4f349e60f6c1b9290
paper_authors.name: Jaegul Choo
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-04-22T10:52:58.097Z
paper_authors._id: 64be3127805e5b64572da65c
paper_authors.avatarUrl: /avatars/edd3e94ba9e375827cc75b164602bcac.svg
paper_authors.fullname: Jaegul Choo
paper_authors.type: user
paper_authors.user: joyfull78
paper_title: SphereDiff: Tuning-free Omnidirectional Panoramic Image and Video
    Generation via Spherical Latent Representation
paper_summary: 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
paper_ai_summary: SphereDiff uses spherical latent representations to generate high-fidelity 360-degree panoramic content by reducing the distortions of equirectangular projection and leveraging pretrained diffusion models.
paper_ai_keywords: spherical latent representation MultiDiffusion equirectangular projection distortion-aware weighted averaging SphereDiff