用压力感应床单实现躺卧时人体三维形状的精准实时重建。
PI-HMR: Towards Robust In-bed Temporal Human Shape Reconstruction with Contact Pressure Sensing
- 结合重力约束与压力分布，生成高质量躺卧人体三维标注数据。
- 通过多尺度特征融合，将关节误差降低17.01mm，优于现有方法。
- 适合医疗健康监测、隐私保护场景下的动态人体建模应用。
长期卧床监测有助于医疗健康领域的自动化实时管理，人体形态重建技术的进步进一步提升了活动模式的表征与可视化能力。然而，现有技术主要依赖视觉线索，在非视线遮挡和隐私敏感的卧床场景中面临严峻挑战。压力感应床单为实时运动重建提供了可行方案，但模型设计与数据积累不足限制了其发展。为此，我们提出一个通用框架，填补数据标注与模型设计间的空白。首先，引入SMPLify-IB优化方法，通过重力约束克服俯视场景中的深度模糊问题，实现高质量的卧床数据集三维人体标注生成。随后提出PI-HMR，一种基于时间序列的压力信号人体形态估计器，通过融合多尺度特征与高压力分布及空间位置先验，相较最先进方法实现17.01mm的平均关节误差下降。本工作构建了从数据到模型的完整解决方案。
原文摘要 · Abstract (English)
Long-term in-bed monitoring benefits automatic and real-time health management within healthcare, and the advancement of human shape reconstruction technologies further enhances the representation and visualization of users' activity patterns. However, existing technologies are primarily based on visual cues, facing serious challenges in non-light-of-sight and privacy-sensitive in-bed scenes. Pressure-sensing bedsheets offer a promising solution for real-time motion reconstruction. Yet, limited exploration in model designs and data have hindered its further development. To tackle these issues, we propose a general framework that bridges gaps in data annotation and model design. Firstly, we introduce SMPLify-IB, an optimization method that overcomes the depth ambiguity issue in top-view scenarios through gravity constraints, enabling generating high-quality 3D human shape annotations for in-bed datasets. Then we present PI-HMR, a temporal-based human shape estimator to regress meshes from pressure sequences. By integrating multi-scale feature fusion with high-pressure distribution and spatial position priors, PI-HMR outperforms SOTA methods with 17.01mm Mean-Per-Joint-Error decrease. This work provides a whole
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