We present a physics-based humanoid controller that achieves high-fidelity motion imitation and fault-tolerant behavior in the presence of noisy input (e.g. pose estimates from video or generated from language) and unexpected falls. Our controller scales up to learning ten thousand motion clips without using any external stabilizing forces and learns to naturally recover from fail-state. Given reference motion, our controller can perpetually control simulated avatars without requiring resets. At its core, we propose the progressive multiplicative control policy (PMCP), which dynamically allocates new network capacity to learn harder and harder motion sequences. PMCP allows efficient scaling for learning from large-scale motion databases and adding new tasks, such as fail-state recovery, without catastrophic forgetting. We demonstrate the effectiveness of our controller by using it to imitate noisy poses from video-based pose estimators and language-based motion generators in a live and real-time multi-person avatar use case.

翻译：本文提出一种基于物理的人体控制方法，能在输入噪声（如视频姿态估计或语言生成姿态）和意外跌倒的情况下实现高保真运动模仿与容错行为。该控制器可扩展至学习一万个运动片段而无需任何外部稳定力，并能自主从失稳状态中恢复。即便参考运动存在噪声，控制器仍能持续驱动仿真虚拟角色而无需复位。其核心创新在于提出渐进式乘法控制策略（PMCP），通过动态分配新增网络容量来学习更复杂的运动序列。PMCP支持从大规模运动数据库进行高效扩展学习，并可在不产生灾难性遗忘的前提下添加新任务（如失稳恢复）。我们通过实时多人在线虚拟角色使用案例验证了本控制器的有效性——成功模仿了基于视频的姿态估计器与基于语言的运动生成器输出的噪声姿态。