Diffusion360: Seamless 360 Degree Panoramic Image Generation based on Diffusion Models (2311.13141v1)

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}

• The paper presents a circular blending strategy within diffusion models to overcome edge discontinuities in 360° panoramic image generation.
• It employs a dual-stage process during denoising and VAE decoding to ensure consistent geometric continuity in generated images.
• Results demonstrate smoother transitions and fewer artifacts, enhancing applications in VR, architectural visualization, and immersive journalism.

Diffusion360: A Method for Seamless 360-Degree Panoramic Image Generation

The paper "Diffusion360: Seamless 360 Degree Panoramic Image Generation based on Diffusion Models" introduces an innovative approach to generating seamless 360-degree panoramic images utilizing diffusion models. In contrast to standard 2D image generation, 360-degree panoramas require the leftmost and rightmost edges to seamlessly connect, presenting unique challenges in maintaining geometric continuity. This work proposes a novel solution through a circular blending strategy integrated into both denoising and VAE decoding stages, addressing the core difficulty of maintaining continuity in such panoramic images.

Methodology Overview

The proposed method is centered on a circular blending strategy designed to enhance geometric continuity during the panoramic image generation process. This technique operates by adaptively blending the latent features at the image's edges, both during denoising and in the VAE decoder's tile decode function. This dual-stage application of circular blending proves crucial for preserving geometric consistency in the resultant panoramic images.

The research outlines the application of the method to two primary tasks:

1. Text-to-360-Panoramas: This task employs a multi-stage framework, initially generating low-resolution images with a base model finetuned on the SUN360 dataset. Subsequent stages involve leveraging super-resolution strategies, such as ControlNet-Tile and RealESRGAN, to upscale these images, ensuring high resolution and maintaining geometric integrity.
2. Single-Image-to-360-Panoramas: The approach adapts the framework from the Text-to-360 task, substituting the base model with a ControlNet-Outpainting model. This task begins with a standard 2D image and transforms it into a low-resolution 360-degree panorama, later enhancing it through super-resolution methods similar to the first task.

Results and Implications

The development of Diffusion360 offers significant improvements in the generation of seamless 360-degree panoramas. The evaluation demonstrates the effectiveness of the circular blending approach, successfully addressing the continuity issues prevalent in previous methods such as MVDiffusion, StitchDiffusion, and PanoDiff. The strategy results in smoother transitions at image edges and fewer artifacts.

Limitations of the paper include the dependency on specific models trained with the DreamBooth technique. This restricts flexibility in applying different styles directly during generation. While style adjustment can be circumvented through additional processing using control networks, it highlights an area for future development concerning direct stylistic integration.

Potential Impact

Diffusion360's integration of circular blending presents a robust framework for panoramic image generation with greater continuity and applicability across different contexts. This advances the state-of-the-art in both text-based and image-augmented virtual environments, facilitating the generation of high-quality 360-degree panoramas.

The implications extend beyond aesthetic continuity, where seamless transition properties can enhance user experience in VR applications and augment real-world scenarios such as architectural visualizations, remote sensing, and immersive journalism. Future research could explore model generalized stylistic adaptation and further integration with real-time image processing capabilities.

By tackling the unique challenges posed by 360-degree imagery, Diffusion360 contributes a well-founded approach, demonstrating practicality and enhancing theoretical understanding within diffusion modeling for panoramic image generation.