SphereDiff Generates Seamless 360° Panoramas Without Fine-Tuning

Seamless 360° Panoramic Images and Videos: SphereDiff Enables Creation Without Fine-Tuning

The increasing demand for Augmented Reality (AR) and Virtual Reality (VR) applications is driving the need for high-quality 360° panoramic content. However, the creation of such content is challenging due to distortions caused by the so-called equirectangular projection (ERP). Previous approaches required either fine-tuning pre-trained diffusion models on limited ERP datasets or relied on distortion-prone, tuning-free methods that also rely on ERP-based latent representations. This often leads to inconsistencies, particularly in the polar regions of the panoramic images.

SphereDiff presents a new approach for generating seamless 360° panoramic images and videos. In contrast to previous methods, SphereDiff does not require additional fine-tuning of state-of-the-art diffusion models. The core of the innovation lies in the definition of a spherical latent representation. This ensures an even distribution across all perspectives and thus minimizes the inherent distortions of ERP.

SphereDiff extends the concept of MultiDiffusion to the spherical latent space and introduces a special spherical sampling method. This allows pre-trained diffusion models to be used directly for panorama generation. Additionally, a distortion-aware weighted averaging improves the quality in the projection process.

Advantages of SphereDiff

The results show that SphereDiff outperforms existing methods in generating 360° panoramic content while maintaining high fidelity. This makes SphereDiff a robust solution for immersive AR/VR applications and opens up new possibilities for the creation of high-quality panoramic content.

By avoiding fine-tuning, SphereDiff reduces the effort required for developing new applications and allows the use of existing, powerful diffusion models. The spherical latent representation addresses the problem of distortions at the poles and leads to seamless, visually compelling panoramas.

Outlook

The development of SphereDiff represents an important advance in the field of 360° content creation. The combination of spherical latent representation, adapted sampling method, and distortion-aware averaging enables the efficient generation of high-quality panoramic images and videos. Future research could focus on further optimizing the spherical representation and integrating additional features to further improve the quality and application range of SphereDiff.

Bibliography: - https://keh0t0.github.io/ - https://arxiv.org/html/2406.13527v2 - https://arxiv.org/html/2406.13527v3 - https://www.researchgate.net/publication/370037357_Immersive_Free-Viewpoint_Panorama_Rendering_from_Omnidirectional_Stereo_Video - https://www.researchgate.net/publication/338757772_Panoramic_Image_Generation_From_2-D_Sketch_to_Spherical_Image