PanoWorld: Real-World Panoramic Generation
In this work, we aim to address the challenge of long-range memory in panoramic world models by exploiting the rotation-equivariant property of omnidirectional representations, where rotation can be treated 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
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- PossiblePossibly related (embedding) · 52%Into the Omniverse: Three Workflows for Improving Vision AI Agent Accuracy With Synthetic Data and Fine-Tuning →
- LinkedLinked via arxiv author · 85%Haoyuan Li →
“PanoWorld: Real-World Panoramic Generation”
- LinkedLinked via arxiv author · 85%Dizhe Zhang →
“PanoWorld: Real-World Panoramic Generation”
- LinkedLinked via arxiv author · 85%Yuemei Zhou →
“PanoWorld: Real-World Panoramic Generation”
- LinkedLinked via arxiv author · 85%Xiangkai Zhang →
“PanoWorld: Real-World Panoramic Generation”
- LinkedLinked via arxiv author · 85%Haoran Feng →
“PanoWorld: Real-World Panoramic Generation”
- LinkedLinked via arxiv author · 85%Xiaofan Lin →
“PanoWorld: Real-World Panoramic Generation”
- LinkedLinked via arxiv author · 85%Wenjie Jiang →
“PanoWorld: Real-World Panoramic Generation”