Video world models are designed to learn how visual environments evolve by predicting missing or future content. A new arXiv paper, Time-JEPA, argues that such models could benefit from physical-state information.
The abstract states that physical-state information can provide useful context for predicting how environments evolve, and notes that prior work has incorporated this idea. However, the available abstract text cuts off before describing how Time-JEPA specifically implements this grounding.
The paper's title suggests a joint embedding predictive architecture (JEPA) that integrates physical time series with video data. As only the abstract's opening is available, this digest is limited to the motivation and the proposed direction.