Diffusion360: Seamless 360 Degree Panoramic Image Generation based on
  Diffusion Models
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}
655eec34a81ce416b9f532d0
Mengyang Feng
admin_assigned
2023-11-23T11:02:54.199Z
63b4ec15103617b0a5b3101e
/avatars/e6faad833b31ad5d892faccf621e7a34.svg
Mengyang Feng
user
archerfmy
655eec34a81ce416b9f532d1
Jinlin Liu
admin_assigned
2023-11-23T11:03:03.808Z
65322c2a7b5d1a9c7b704c27
/avatars/8c686fa54e7a61594bee20791177b6c1.svg
Liu
user
Jinlin123
655eec34a81ce416b9f532d2
Miaomiao Cui
655eec34a81ce416b9f532d3
Xuansong Xie
Diffusion360: Seamless 360 Degree Panoramic Image Generation based on
  Diffusion Models
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}
A circular blending strategy is introduced to generate seamless 360-degree panoramic images using diffusion models for both text-to-360-panoramas and single-image-to-360-panoramas tasks.
diffusion models 360-degree panoramic images circular blending strategy denoising VAE decoding text-to-360-panoramas single-image-to-360-panoramas