NavGen: Visual Generative Models as a Scalable Data Engine for Embodied 3D Navigation
General-purpose robot models increasingly rely on large and diverse datasets. For embodied 3D navigation, however, existing data sources face a fundamental trade-off: simulated data can be generated at scale but often suffer from the visual sim-to-real gap, whereas real-world flight data provide realistic observations but are costly to collect. This paper studies another direction: the use of high-fidelity visual generative models as scalable data engines for embodied 3D navigation. We introduce NavGen, a text-to-video data generation pipeline that produces diverse vision-language navigation (VLN) episodes across indoor and outdoor scenes. We also propose a style-diversification method th...
| Authors | Xijie Huang, Yongyang Wan, Chengbin Dong et al. |
|---|---|
| Primary category | cs.RO |
| Published | 25 September 2026 |
| Last updated at source | 25 September 2026 |
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