Moving from Space to Time: Forcing models to learn persistence and dynamics by preventing "Temporal Cheating."
Predicting video is harder than predicting images because models can "cheat." If you mask random pixels in a video, the model doesn't need to understand motion; it can just look at the exact same pixel in the previous or next frame to find the answer.
**V-JEPA** solves this using Tubelet Masking: hiding the same spatial area across the entire temporal duration of the video block. This forces the model to learn World Physics—the understanding that an object continues to exist and follow a trajectory even when it is completely hidden.
In this mode, the moving object is never fully hidden. The model can simply 'see through' the noise by comparing adjacent frames. It learns to interpolate pixels, not to model the world.
A true World Model understands that objects continue to exist even when they are occluded. While a generative model must "hallucinate" pixels to fill gaps, JEPA maintains a stable latent representation of the object's identity and state.
Watch as the ball passes behind the wall. In the Input view, the pixels are literally gone. However, the Latent Saliency Map continues to track the object's probability distribution using inertia and world-model physics. This is why V-JEPA is robust for navigation in dynamic environments.