LoopNav: Benchmarking Spatial Consistency in World Models

Understand

The ability to simulate the world in a spatially consistent manner is a crucial requirement for effective world models.

• Such a model enables high-quality visual generation, and also ensures the reliability of world models for downstream tasks such as simulation and planning.
• It must not only retain long-horizon observational information, but also enables the construction of explicit or implicit internal spatial representations.
• However, existing datasets do not explicitly enforce spatial consistency constraints, limiting both the ability to systematically evaluate this capability and to learn it through data-driven approaches.