用多设备压力图实现高精度3D人体姿态重建
PressureMesh: 3D Human Mesh Estimation from Multi-Device Pressure Images
- 设计端到端网络，融合多设备压力数据进行人体网格估计
- 在自建数据集上实现12.6厘米关节定位误差
- 适合隐私敏感场景下的日常姿态监测应用
人体姿态监测在康复评估和人机交互等领域至关重要。由于具有隐私保护优势，基于压力的无感监测已成为主流方法。然而现有方法通常仅依赖单一设备，限制了有效监测范围。为此，我们提出MDP-Net，一种可直接从多设备时序压力数据中估计人体网格的端到端网络。引入受专家混合（MoE）框架启发的多模态融合机制，实现跨设备压力信息的有效互补与增强。为支持MDP-Net的训练与评估，我们构建了高质量的多设备时序压力数据集MDP，包含2D/3D关节与人体网格等多种标注。实验表明，MDP-Net在MDP数据集上达到12.6厘米的关节位置误差。结果证明，融合多设备压力信息是日常人体姿态监测的一种有效且有前景的新方案。
原文摘要 · Abstract (English)
Human pose monitoring is crucial in fields such as rehabilitation assessment and human-computer interaction. Due to its privacy-preserving nature, pressure-based human pose monitoring has become a primary approach for unobtrusive sensing. However, existing methods are generally limited to a single device, which restricts the effective monitoring range. To address this limitation, we propose MDP-Net, an end-to-end network capable of directly estimating human meshes from temporal pressure data across multiple devices. We introduce a multimodal fusion mechanism inspired by the Mixture of Experts (MoE) framework to achieve effective complementarity and enhancement of cross-device pressure information. To support the training and evaluation of MDP-Net, we constructed MDP, a high-quality multi-device temporal pressure dataset that includes various pose labels such as 2D/3D joints and human meshes. Experimental results demonstrate that MDP-Net achieves a joint position error of 12.6 cm on the MDP dataset. These results prove that fusing multi-device pressure information is an effective and promising new solution for daily human pose monitoring.
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