Neural Atlas Graphs are a hybrid representation enabling high-resolution texture and positional editing of dynamic scenes, achieving SOTA results on automotive datasets (Waymo) while demonstrating strong generalization on outdoor scenes (DAVIS).

We introduce a framework for in-context (zero-shot) inference/estimation of drift and diffusion functions underlying SDEs from empirical data of different dimensionalities.

We present a novel deep learning model for global river discharge and flood forecasting