SRBTrack: Terrain-Adaptive Tracking of a Single-Rigid-Body Character Using Momentum-Mapped Space-Time Optimization

Abstract

Generating realistic and robust motion for virtual characters under complex physical conditions, such as irregular terrain, real-time control scenarios, and external disturbances, remains a key challenge in computer graphics. While deep reinforcement learning has enabled high-fidelity physics-based character animation, such methods often suffer from limited generalizability, as learned controllers tend to overfit to the environments they were trained in. In contrast, simplified models, such as single rigid bodies, offer better adaptability, but traditionally require hand-crafted heuristics and can only handle short motion segments. In this paper, we present a general learning framework that trains a single-rigid-body (SRB) character controller from long and unstructured datasets, without the reliance on human-crafted rules. Our method enables zero-shot adaptation to diverse environments and unseen motion styles. The resulting controller generates expressive and physically plausible motions in real time and seamlessly integrates with high-level kinematic motion planners without retraining, enabling a wide range of downstream tasks.