SphereDiff: Tuning-free Omnidirectional Panoramic Image and Video
  Generation via Spherical Latent Representation
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
6806fcc4f349e60f6c1b928c
Minho Park
claimed_verified
2025-04-22T09:50:59.880Z
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Minho Park
user
mpark
6806fcc4f349e60f6c1b928d
Taewoong Kang
admin_assigned
2025-04-22T10:52:46.869Z
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TaewoongKang
user
TaewoongKang
6806fcc4f349e60f6c1b928e
Jooyeol Yun
claimed_verified
2025-04-22T09:51:02.918Z
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/avatars/e937dc8234b3e456149882bfce34841f.svg
Jooyeol Yun
user
YeolJoo
6806fcc4f349e60f6c1b928f
Sungwon Hwang
admin_assigned
2025-04-22T10:52:52.332Z
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Sungwon Hwang
user
sungwon95
6806fcc4f349e60f6c1b9290
Jaegul Choo
admin_assigned
2025-04-22T10:52:58.097Z
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/avatars/edd3e94ba9e375827cc75b164602bcac.svg
Jaegul Choo
user
joyfull78
SphereDiff: Tuning-free Omnidirectional Panoramic Image and Video
  Generation via Spherical Latent Representation
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
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.
spherical latent representation MultiDiffusion equirectangular projection distortion-aware weighted averaging SphereDiff