从稀疏的惯性测量单位来重建人类快速运动,考虑到人类形状
Xuan Xiao1,2, Jianjian Wang1,2, Pingfa Feng1,2
1State Key Laboratory of Tribology in Advanced Equipment, Department of Mechanical Engineering, Tsinghua University, 100084, Beijing, China.
Nature communications
|March 19, 2024
概括
快速惯性姿势器使用稀疏的惯性测量单位 (IMU) 进行高效的全身运动估计. 这种深度学习模型显著降低了延迟,并提高了实时应用程序的计算速度.
科学领域:
- 计算机视觉 计算机视觉
- 生物机械工程 生物机械工程
- 机器学习 机器学习
背景情况:
- 基于惯性测量单元 (IMU) 的运动捕捉为复杂环境提供了潜力.
- 稀少的IMU方法是灵活的,但在计算效率和延迟方面面临挑战.
- 准确和高效的全身运动估计对于各种应用至关重要.
研究的目的:
- 提出Fast Inertial Poser,一种新的深度神经网络,用于使用稀疏的IMU高效地估计全身运动.
- 解决现有的稀疏IMU-based方法的计算效率和延迟限制.
- 为了提高人类运动重建的准确性和实时性能.
主要方法:
- 开发了一个深度神经网络,利用循环神经网络和动力学树结构.
- 整合了人体形状信息,并利用因果观察来消除未来的框架依赖.
- 采用单独的网络模块用于上部和下部的联合估计.
- 实现了一个单动力学反向解决器,用于关节旋转估计.
主要成果:
- 与以前的方法相比,Fast Inertial Poser显著提高了推断速度,并减少了延迟时间.
- 该模型在保持效率的同时实现了高的重建精度.
- 在嵌入式计算机上展示了实时性能,每秒65 (fps) 和15毫秒 (ms) 延迟.
结论:
- 快速惯性姿势器为基于IMU的稀疏全身运动估计提供了高效和准确的解决方案.
- 拟议的方法克服了计算效率和延迟方面的关键挑战.
- 这一进步有望在各种应用中实现实时运动捕捉.
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