4D-WAM: 4D Consistent World Modeling for Autonomous Driving
Leveraging geometric foundation models to bridge the gap between 2D video prediction and 4D physical consistency.
Emerging World-Action Models (WAMs) have demonstrated promising performance in autonomous driving by jointly modeling future driving scene evolution and trajectory planning. However, existing WAMs are typically trained with video data, which is only 2D projections of the underlyi...
ArXiv CS.CV
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By integrating geometric foundation models into world-action modeling, researchers have bridged the gap between 2D video prediction and the physical reality of spatial navigation. This advancement ensures that autonomous systems make planning decisions based on physically consistent environments rather than mere visual illusions.
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