World-State Prediction

Train models to predict future 3D states of a scene from current observations, enabling physics-aware 3D forecasting for robotics, autonomous driving, and simulation. World-state prediction extends next-frame prediction from 2D pixels to full 3D scene representations. Given a sequence of 3D observations (point clouds, voxel grids, or neural fields), the model learns a dynamics model that predicts the next 3D state. This is trained on sequences of real or simulated 3D data with a reconstruction or occupancy loss. The predicted 3D state can be rendered from any viewpoint, enabling the model to “imagine” what will happen next in 3D.