title: Diffusion360: Seamless 360 Degree Panoramic Image Generation based on
    Diffusion Models
summary: This is a technical report on the 360-degree panoramic image generation task
  based on diffusion models. Unlike ordinary 2D images, 360-degree panoramic
  images capture the entire 360^circtimes 180^circ field of view. So the
  rightmost and the leftmost sides of the 360 panoramic image should be
  continued, which is the main challenge in this field. However, the current
  diffusion pipeline is not appropriate for generating such a seamless 360-degree
  panoramic image. To this end, we propose a circular blending strategy on both
  the denoising and VAE decoding stages to maintain the geometry continuity.
  Based on this, we present two models for Text-to-360-panoramas and
  Single-Image-to-360-panoramas tasks. The code has been released as an
  open-source project at
  https://github.com/ArcherFMY/SD-T2I-360PanoImage{https://github.com/ArcherFMY/SD-T2I-360PanoImage}
  and
  https://www.modelscope.cn/models/damo/cv_diffusion_text-to-360panorama-image_generation/summary{ModelScope}
paper_authors._id: 655eec34a81ce416b9f532d0
paper_authors.name: Mengyang Feng
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-11-23T11:02:54.199Z
paper_authors._id: 63b4ec15103617b0a5b3101e
paper_authors.avatarUrl: /avatars/e6faad833b31ad5d892faccf621e7a34.svg
paper_authors.fullname: Mengyang Feng
paper_authors.type: user
paper_authors.user: archerfmy
paper_authors._id: 655eec34a81ce416b9f532d1
paper_authors.name: Jinlin Liu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-11-23T11:03:03.808Z
paper_authors._id: 65322c2a7b5d1a9c7b704c27
paper_authors.avatarUrl: /avatars/8c686fa54e7a61594bee20791177b6c1.svg
paper_authors.fullname: Liu
paper_authors.type: user
paper_authors.user: Jinlin123
paper_authors._id: 655eec34a81ce416b9f532d2
paper_authors.name: Miaomiao Cui
paper_authors._id: 655eec34a81ce416b9f532d3
paper_authors.name: Xuansong Xie
paper_title: Diffusion360: Seamless 360 Degree Panoramic Image Generation based on
    Diffusion Models
paper_summary: This is a technical report on the 360-degree panoramic image generation task
  based on diffusion models. Unlike ordinary 2D images, 360-degree panoramic
  images capture the entire 360^circtimes 180^circ field of view. So the
  rightmost and the leftmost sides of the 360 panoramic image should be
  continued, which is the main challenge in this field. However, the current
  diffusion pipeline is not appropriate for generating such a seamless 360-degree
  panoramic image. To this end, we propose a circular blending strategy on both
  the denoising and VAE decoding stages to maintain the geometry continuity.
  Based on this, we present two models for Text-to-360-panoramas and
  Single-Image-to-360-panoramas tasks. The code has been released as an
  open-source project at
  https://github.com/ArcherFMY/SD-T2I-360PanoImage{https://github.com/ArcherFMY/SD-T2I-360PanoImage}
  and
  https://www.modelscope.cn/models/damo/cv_diffusion_text-to-360panorama-image_generation/summary{ModelScope}
paper_ai_keywords: diffusion models 360-degree panoramic images field of view denoising VAE decoding geometry continuity Text-To-360-panoramas Single-Image-to-360-panoramas