MAGNET: Muscle Activation Generation Networks for Diverse Human Movement
Jungnam Park, Euikyun Jung, Jehee Lee, Jungdam Won
2025年份
3被引次数
2顶会引用
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.
问问这篇 Paper
问问你的智能体。
Lune 读过与它相关的顶会 Paper,每个回答都会注明依据哪几篇。
引用它的顶会 Paper2
- Exo-Plore: Exploring Exoskeleton Control Space through Human-aligned SimulationGeonho Leem, Jaedong Lee, Jehee Lee, Seungmoon Song 等ICLR 2026 · 被引用 11 次
- Scalable Exploration for High-Dimensional Continuous Control via Value-Guided FlowYunyue Wei, Chenhui Zuo, Yanan SuiICLR 2026 · 被引用 8 次
相关 Paper
- Learning active quasistatic physics-based models from dataSangeetha Grama Srinivasan, Qisi Wang, Junior Rojas, Gergely Klár 等SIGGRAPH 2021 · 被引用 20 次
- ASE: large-scale reusable adversarial skill embeddings for physically simulated charactersXue Bin Peng, Yunrong Guo, Lina Halper, Sergey Levine 等SIGGRAPH 2022 · 被引用 217 次
- Bidirectional GaitNet: A Bidirectional Prediction Model of Human Gait and Anatomical ConditionsJungnam Park, Moon Seok Park, Jehee Lee, Jungdam WonSIGGRAPH 2023 · 被引用 8 次
- Generative GaitNetJungnam Park, Sehee Min, Phil Sik Chang, Jaedong Lee 等SIGGRAPH 2022 · 被引用 22 次
- ModSkill: Physical Character Skill ModularizationYiming Huang, Zhiyang Dou, Lingjie LiuICCV 2025 · 被引用 1 次
