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arXiv · 2501.17162

CubeDiff: Repurposing Diffusion-Based Image Models for Panorama Generation

Nikolai Kalischek·Michael Oechsle·Fabian Manhardt·Philipp Henzler·Konrad Schindler·Federico Tombari
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Abstract

We introduce a novel method for generating 360{\deg} panoramas from text prompts or images. Our approach leverages recent advances in 3D generation by employing multi-view diffusion models to jointly synthesize the six faces of a cubemap. Unlike previous methods that rely on processing equirectangular projections or autoregressive generation, our method treats each face as a standard perspective image, simplifying the generation process and enabling the use of existing multi-view diffusion models. We demonstrate that these models can be adapted to produce high-quality cubemaps without requiring correspondence-aware attention layers. Our model allows for fine-grained text control, generates high resolution panorama images and generalizes well beyond its training set, whilst achieving state-of-the-art results, both qualitatively and quantitatively. Project page: https://cubediff.github.io/

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Nikolai Kalischek, Michael Oechsle, Fabian Manhardt, Philipp Henzler, Konrad Schindler, Federico Tombari. 2025-01-28. CubeDiff: Repurposing Diffusion-Based Image Models for Panorama Generation. https://arxiv.org/abs/2501.17162

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