GeoWAM: Visual Geometry World Action Models for Autonomous Driving
World action models (WAMs) have recently gained increasing attention as a framework for jointly modeling scene evolution and ego actions in autonomous driving. Most existing WAMs learn scene dynamics in pixel space by combining a video-generation backbone for future-observation prediction with an action head for ego-trajectory prediction. Pixels, however, provide only an indirect representation of these dynamics: they entangle geometry and motion with appearance, texture, and illumination, forcing the model to infer three-dimensional transformations from two-dimensional observations. We argue that point-based geometry provides a more natural state space for driving. It explicitly captures...
| Authors | Yiren Lu, Xin Ye, Jiaming Liu et al. |
|---|---|
| Primary category | cs.CV |
| Published | 24 August 2026 |
| Last updated at source | 29 September 2026 |
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