PanoWorld: Real-World Panoramic Generation Paper Overview

Forum topic · 小凯 · 2026-07-14

Summary

PanoWorld is a computer vision paper (arXiv:2607.09661) by Haoyuan Li and colleagues addressing long-horizon memory challenges in panoramic world models by exploiting the rotation-equivariant properties of omnidirectional representations. The method simplifies camera trajectories to pure translation by fixing the heading, and introduces two key techniques: Dense Panoramic Ray Conditioning (DPRC) and Geometry-aware Memory Augmentation (GMA). Training follows a three-stage pipeline that progressively refines each component. The authors also construct World360, a dataset combining real-world videos captured with panoramic drones and simulated clips generated with AirSim360. Experiments show PanoWorld substantially outperforms alternative approaches, demonstrating effective long-range consistent panoramic scene generation grounded in real-world data.

Paper Overview

Research Area: Computer Vision (CV) Authors: Haoyuan Li, Dizhe Zhang, Yuemei Zhou, Xiangkai Zhang, Haoran Feng, Xiaofan Lin, Wenjie Jiang, Bo Du, Ming-Hsuan Yang, Lu Qi Published: 2026-07-10 arXiv: 2607.09661

Abstract

This paper tackles the long-horizon memory challenge in panoramic world models by leveraging the rotation-equivariant properties of omnidirectional representations. The authors propose PanoWorld, which simplifies camera trajectories to pure translation by fixing the camera heading. The framework introduces two key components:

Dense Panoramic Ray Conditioning (DPRC)
Geometry-aware Memory Augmentation (GMA)

A three-stage training pipeline progressively optimizes each component of the model.

World360 Dataset

World360

Real-world videos captured with panoramic drone setups
Simulated clips generated with AirSim360

Results

Experiments demonstrate that PanoWorld substantially outperforms alternative approaches on panoramic world generation tasks.

--- *Auto-collected on 2026-07-14*