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Diffusion360: Seamless 360 Degree Panoramic Image Generation based on Diffusion Models

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arxiv 2311.13141 v1 pith:4LYXQLYU submitted 2023-11-22 cs.CV

Mengyang Feng , Jinlin Liu , Miaomiao Cui , Xuansong Xie This is my paper
classification cs.CV
keywords panoramicdegreediffusionimagemodelsgenerationhttpsarcherfmy
verification ladder T0 review T1 audit T2 compute T3 formal
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abstract

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 $360^\circ\times 180^\circ$ 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 \textbf{Text-to-360-panoramas} and \textbf{Single-Image-to-360-panoramas} tasks. The code has been released as an open-source project at \href{https://github.com/ArcherFMY/SD-T2I-360PanoImage}{https://github.com/ArcherFMY/SD-T2I-360PanoImage} and \href{https://www.modelscope.cn/models/damo/cv_diffusion_text-to-360panorama-image_generation/summary}{ModelScope}

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Cited by 11 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. ABot-3DWorld 0: A Universal World Model to Explore Any 3D Space

    cs.CV 2026-07 unverdicted novelty 6.0 of 10

    A unified pipeline lifts any text/image/video input into a Spatial Generative Primitive, explores it with 3D-consistent panoramic video, and reconstructs photorealistic 3DGS worlds with stronger rich-input fidelity th...

  2. 360Anything: Geometry-Free Lifting of Images and Videos to 360{\deg}

    cs.CV 2026-01 conditional novelty 6.0 of 10

    360Anything lifts perspective images and videos to 360° panoramas with a diffusion transformer and sequence concatenation, requiring no camera metadata at test time.

  3. Look Beyond: Two-Stage Scene View Generation via Panorama and Video Diffusion

    cs.CV 2025-08 conditional novelty 6.0 of 10

    Single-image novel view synthesis is decomposed into panorama outpainting plus keyframe-conditioned video diffusion, producing loop-consistent scene tours.

  4. Top2Pano: Learning to Generate Indoor Panoramas from Top-Down View

    cs.CV 2025-07 conditional novelty 6.0 of 10

    Top2Pano generates photorealistic 360-degree indoor panoramas from top-down floorplan-like views by estimating 3D occupancy, volume-rendering coarse views, and refining them with a ControlNet diffusion model.

  5. 3D-Generalist: Self-Improving Vision-Language-Action Models for Crafting 3D Worlds

    cs.GR 2025-07 conditional novelty 6.0 of 10

    A self-improving vision-language-model policy iteratively crafts 3D environments from text, and renderings of those environments serve as effective synthetic pretraining data for vision models.

  6. ViewPoint: Panoramic Video Generation with Pretrained Diffusion Models

    cs.CV 2025-06 conditional novelty 6.0 of 10

    A panorama representation and attention scheme that lets a pretrained perspective video diffusion model generate spatially consistent 360-degree videos from an input perspective clip.

  7. TanDiT: Tangent-Plane Diffusion Transformer for High-Quality 360{\deg} Panorama Generation

    cs.CV 2025-06 conditional novelty 6.0 of 10

    TanDiT generates high-quality 360-degree panoramas by jointly generating grids of tangent-plane views with a single diffusion transformer and refining them with a pretrained model.

  8. WonderFree: Enhancing Novel View Quality and Cross-View Consistency for 3D Scene Exploration

    cs.CV 2025-06 conditional novelty 6.0 of 10

    A pipeline that restores corrupted novel-view videos with a video diffusion model and jointly denoises multiple viewpoints to improve 3D scene exploration from a single image.

  9. DreamCube: 3D Panorama Generation via Multi-plane Synchronization

    cs.GR 2025-06 conditional novelty 6.0 of 10

    A synchronized multi-plane adaptation of 2D diffusion operators enables seam-consistent cubemap generation, and DreamCube extends this to joint RGB-D panorama generation and 3D scene lifting.

  10. Gimbal360: Canonicalizing Planar Diffusion for Spherical Panorama Completion

    cs.CV 2026-03 reject novelty 5.0 of 10

    Gimbal360 completes 360° panoramas from unposed perspective images by rigidly auto-leveling inputs and training diffusion with a Siamese shift-equivariance loss to preserve ERP seam continuity.

  11. HunyuanWorld 1.0: Generating Immersive, Explorable, and Interactive 3D Worlds from Words or Pixels

    cs.CV 2025-07 conditional novelty 5.0 of 10

    A staged pipeline generates layered, mesh-based 3D worlds from text or images by combining panoramic diffusion, semantic layer decomposition, and video-based expansion.

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