Ziyu Wu, Fangting Xie, Yiran Fang, Zhen Liang, Quan Wan, Yufan Xiong, Xiaohui Cai
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引用次数: 0
摘要
人类一生中约有三分之一的时间在休息。在睡眠研究、褥疮监测和生物医学因素提取中,重建床上场景中的人体动态具有相当重要的意义。然而,主流的人体姿态和形状估计方法主要侧重于视觉线索,在非视线环境中面临严重问题。由于床上场景包含复杂的人与环境接触,压力传感床单提供了一种无创、保护隐私的方法来捕捉接触面的压力分布,并在许多下游任务中展现了前景。然而,很少有研究关注床上人体网状结构的恢复。为了探索从感应到的压力分布重建人体网格的潜力,我们首先建立了一个包含 152K 张多模态同步图像的高质量时态床内人体姿态数据集 TIP。然后,我们提出了床上场景的标签生成管道,利用基于 SMPLify 的优化器生成可靠的 3D 网格标签。最后,我们介绍了 PIMesh,这是一种简单而有效的时间人体形状估计器,可直接从压力图像序列生成人体网格。我们进行了各种实验来评估 PIMesh 的性能,结果表明 PIMesh 在 TIP 数据集上归档了 79.17 毫米的关节位置误差。结果表明,压力传感床单可作为长期床内人体形状估计的一种有前途的替代方法。
Humans spend about one-third of their lives resting. Reconstructing human dynamics in in-bed scenarios is of considerable significance in sleep studies, bedsore monitoring, and biomedical factor extractions. However, the mainstream human pose and shape estimation methods mainly focus on visual cues, facing serious issues in non-line-of-sight environments. Since in-bed scenarios contain complicated human-environment contact, pressure-sensing bedsheets provide a non-invasive and privacy-preserving approach to capture the pressure distribution on the contact surface, and have shown prospects in many downstream tasks. However, few studies focus on in-bed human mesh recovery. To explore the potential of reconstructing human meshes from the sensed pressure distribution, we first build a high-quality temporal human in-bed pose dataset, TIP, with 152K multi-modality synchronized images. We then propose a label generation pipeline for in-bed scenarios to generate reliable 3D mesh labels with a SMPLify-based optimizer. Finally, we present PIMesh, a simple yet effective temporal human shape estimator to directly generate human meshes from pressure image sequences. We conduct various experiments to evaluate PIMesh's performance, showing that PIMesh archives 79.17mm joint position errors on our TIP dataset. The results demonstrate that the pressure-sensing bedsheet could be a promising alternative for long-term in-bed human shape estimation.