PI-HMR：面向鲁棒的基于接触压力传感的卧床时序人体形状重建
PI-HMR: Towards Robust In-bed Temporal Human Shape Reconstruction with Contact Pressure Sensing
AI总结：
针对卧床场景下视觉人体重建存在的非视距、隐私敏感等问题，提出结合压力传感的通用框架，含SMPLify-IB标注方法与PI-HMR时序重建模型，性能优于SOTA方法。
摘要：
长期卧床监测有助于医疗领域的自动化实时健康管理，而人体形状重建技术的发展进一步提升了用户活动模式的表征与可视化效果。然而，现有技术主要基于视觉线索，在非视距且隐私敏感的卧床场景中面临严峻挑战。压力传感床单为实时运动重建提供了一种有前景的解决方案，但模型设计与数据方面的探索有限，阻碍了其进一步发展。为解决这些问题，我们提出了一个通用框架，填补了数据标注与模型设计方面的空白。首先，我们提出SMPLify-IB这一优化方法，它通过重力约束克服了俯视场景中的深度模糊问题，能够为卧床数据集生成高质量的3D人体形状标注。随后，我们提出PI-HMR，一种基于时序的人体形状估计器，用于从压力序列中回归网格。通过将多尺度特征融合与高压分布、空间位置先验相结合，PI-HMR的性能优于现有SOTA（state-of-the-art，当前最优）方法，平均关节误差降低了17.01mm。本研究提供了一套完整的
英文摘要：
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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