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：我们提出了一种基于物理的类人控制器，能够在存在噪声输入（例如来自视频的姿态估计或由语言生成的姿态）和意外跌倒的情况下，实现高保真动作模仿与容错行为。该控制器无需使用任何外部稳定力即可扩展学习上万条动作片段，并能够自然地从失效状态中恢复。在给定参考动作的情况下，控制器可永续控制仿真虚拟人而无需重置。其核心是渐进式乘性控制策略（PMCP），该策略动态分配新网络容量以学习难度递增的动作序列。PMCP支持从大规模动作数据库进行高效扩展学习，并能添加如失效状态恢复等新任务，同时避免灾难性遗忘。我们通过现场实时多虚拟人应用场景中的演示验证了该控制器的有效性——使用基于视频的姿态估计器和基于语言的动作生成器产生的噪声姿态进行模仿。