PanoWorld: Panoramic World Generation
Insta360 · hf · 2026-07-13
This work introduces PanoWorld for real-world panoramic generation, aiming to solve the long-range memory problem in panoramic world models.
Key contributions include:
- Leveraging the rotational equivariance of omnidirectional representations to convert rotations into implicit geometric transformations.
- Designing DPRC (Dense Panoramic Ray-Conditioning) and GMA (Geometry-aware Memory Augmentation).
- Proposing a three-stage training pipeline to progressively optimize each module.
- Constructing the World360 dataset, featuring real drone panoramic videos and high-quality simulated clips generated via AirSim360.
Extensive experiments on World360 show that PanoWorld significantly outperforms existing methods. The models, training code, and datasets are planned for public release.
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