Pose2met:一个统一的时空框架,用于3D人体姿势估计和能源支出估计
Zhongteng Zhang1, Liu Zhang1, Qing Peng1
1School of Computer Science and Engineering, Central South University, Changsha, China.
Health information science and systems
|February 4, 2026
概括
本研究介绍了Pose2Met,这是3D人体姿势估计 (HPE) 和能源消耗估计 (EEE) 的统一框架. 它有效地模拟运动和新陈代谢,提高适合健身和医疗保健应用的准确性和稳定性.
科学领域:
- 计算机视觉 计算机视觉
- 生物医学工程 生物医学工程
- 机器学习 机器学习
背景情况:
- 准确的3D人体姿势估计 (HPE) 和能量消耗估计 (EEE) 对健身和医疗保健至关重要.
- 现有的方法经常面临复杂活动和概括性的挑战.
- 在统一的框架内共同优化HPE和EEE仍然是一个开放的研究领域.
研究的目的:
- 通过开发一个统一的框架来应对3DHPE和EEE的挑战.
- 改善复杂活动的处理和增强概括能力.
- 共同优化姿势动态和代谢模式,以便从二维姿势输入直接预测.
主要方法:
- 提出Pose2Met,一个统一的端到端框架,用于共同的3DHPE和EEE.
- 介绍了STAPFormer,这是一个使用STAP (空间时空聚合姿势) 表示的变压器模型,用于运动建模.
- 实施了一种统一的姿势代谢学习策略,用于联合优化.
主要成果:
- 在Human3.6M上,STAPFormer实现了38.2毫米的MPJPE,超过了现有的模型.
- EEE预测在Vid2Burn-ADL上使用基于姿势的输入实现了22.1kcal的MAE.
- 统一的框架表现出增强的稳定性和通用性,基于2D姿势的EEE接近基于3D姿势的准确性.
结论:
- 高质量的运动表示对于HPE和EEE都至关重要.
- Pose2Met显示出智能健身和医疗保健应用的巨大潜力.
- 该框架提供了一个有前途的方向,可以弥合提出和支出估计之间的差距.
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