PanoWorld: Real-World Panoramic Generation

In this work, we aim to address the challenge of controllable panoramic world modeling by leveraging the rotation-equivariant properties of omnidirectional representations, where camera rotation can be naturally modeled as an implicit geometric transformation. Building on this insight, we propose PanoWorld, which simplifies camera trajectories into translations via fixed headings for both current-action modeling and long-range memory through Dense Panoramic Ray-Conditioning (DPRC) and Geometry-aware Memory Augmentation (GMA). Then, a three-stage training pipeline is introduced to progressively optimize each component. To better evaluate physical consistency under large-scale spatial variations and diverse real-world environments with varying lighting scenarios, we construct World360, a large-scale dataset consisting of both real-world video clips collected via panoramic unmanned aerial vehicles and high-quality simulated clips generated by AirSim360. Extensive experiments on World360 demonstrate the effectiveness of PanoWorld, achieving superior generation quality while maintaining strong controllability. Our models, training code, and dataset will be publicly available.