リアルタイムシミュレーションアバターのための永続的人型制御
Perpetual Humanoid Control for Real-time Simulated Avatars
ノイズの多い入力や転倒時でも高精度な動作模倣と復帰を実現する物理ベース人型コントローラを提案し、大規模動作データから効率的に学習する漸進的乗算制御方策を導入した。
著者: Zhengyi Luo, Jinkun Cao, Alexander Winkler, Kris Kitani, Weipeng Xu
分類: cs.CV, cs.GR, cs.RO
原文アブストラクト
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.
