MAGNET: Muscle Activation Generation Networks for Diverse Human Movement
Jungnam Park, Euikyun Jung, Jehee Lee, Jungdam Won
2025Year
3Citations
2Top-tier citations
Abstract
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.
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Cited by top-tier papers2
- Exo-Plore: Exploring Exoskeleton Control Space through Human-aligned SimulationGeonho Leem, Jaedong Lee, Jehee Lee, Seungmoon Song et al.ICLR 2026 · 11 citations
- Scalable Exploration for High-Dimensional Continuous Control via Value-Guided FlowYunyue Wei, Chenhui Zuo, Yanan SuiICLR 2026 · 8 citations
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