MAGNET: Muscle Activation Generation Networks for Diverse Human Movement

摘要

We introduce MAGNET (Muscle Activation Generation Networks), a scalable framework for reconstructing full-body muscle activations across diverse human movements. Our approach employs musculoskeletal simulation with a novel two-level controller architecture trained using three-stage learning methods. Additionally, we develop distilled models tailored for solving downstream tasks or generating real-time muscle activations, even on edge devices. The efficacy of our framework is demonstrated through examples of daily life and challenging behaviors, as well as comprehensive evaluations.