Stochastic Video Prediction

Stochastic video prediction aims to forecast future video frames while explicitly accounting for inherent uncertainties in the scene's dynamics. Current research emphasizes developing models that effectively capture long-term temporal dependencies and disentangle scene structure (static elements) from motion (dynamic elements), often employing state-space models, diffusion models, or contrastive predictive coding approaches. These advancements improve prediction accuracy, particularly in complex scenarios with moving objects and cameras, and have implications for applications such as wildfire forecasting and autonomous driving.