用仿真生成九小时人体肌肉激活数据，助力动作理解研究。
Muscles in Time: Learning to Understand Human Motion by Simulating Muscle Activations
- 基于OpenSim平台模拟人体肌肉激活，构建大规模合成数据集
- 涵盖227名受试者、402条肌肉轨迹，总时长超9小时
- 支持从姿态序列预测肌肉激活，适合运动分析与生物力学研究
探索肌肉与骨骼结构间的复杂动态是理解人类运动的关键。然而，真实肌肉激活数据获取成本高，导致数据稀缺。本文提出Muscles in Time（MinT），一个大规模合成肌肉激活数据集。通过在现有动作捕捉数据基础上，利用OpenSim平台的生物力学人体模型进行肌肉激活仿真，从简单姿态序列中提取肌肉激活时间信息。MinT包含超过九小时的仿真数据，覆盖227名受试者和402条模拟肌肉轨迹。我们展示了该数据集在神经网络驱动的肌肉激活估计任务中的应用，采用两种不同的序列到序列架构，验证了其有效性。数据与代码已公开于https://simplexsigil.github.io/mint。
原文摘要 · Abstract (English)
Exploring the intricate dynamics between muscular and skeletal structures is pivotal for understanding human motion. This domain presents substantial challenges, primarily attributed to the intensive resources required for acquiring ground truth muscle activation data, resulting in a scarcity of datasets. In this work, we address this issue by establishing Muscles in Time (MinT), a large-scale synthetic muscle activation dataset. For the creation of MinT, we enriched existing motion capture datasets by incorporating muscle activation simulations derived from biomechanical human body models using the OpenSim platform, a common approach in biomechanics and human motion research. Starting from simple pose sequences, our pipeline enables us to extract detailed information about the timing of muscle activations within the human musculoskeletal system. Muscles in Time contains over nine hours of simulation data covering 227 subjects and 402 simulated muscle strands. We demonstrate the utility of this dataset by presenting results on neural network-based muscle activation estimation from human pose sequences with two different sequence-to-sequence architectures. Data and code are provided under https://simplexsigil.github.io/mint.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。