用惯性传感器和足底压力数据，还原真实物理动作。
Ground Reaction Inertial Poser: Physics-based Human Motion Capture from Sparse IMUs and Insole Pressure Sensors
- 结合惯性与足底压力数据，构建物理可解释的人体运动模型。
- 在多个数据集上精度超越现有方法，且动作更符合物理规律。
- 适合穿戴式人体动作捕捉、运动康复等需要真实物理行为的场景。
我们提出一种名为地面反作用惯性姿态生成器（GRIP）的方法，通过四个可穿戴设备重建物理上合理的运动。与传统仅依赖惯性测量单元（IMU）的方法不同，GRIP融合了IMU信号与足底压力数据，以捕捉身体动态与地面相互作用。它不依赖纯运动学估计，而是采用一个物理仿真中的合成人形数字孪生体来生成真实可信的动作。核心由两个模块构成：KinematicsNet从传感器数据中估计身体姿态与速度；DynamicsNet则通过比较预测值与仿真状态的残差，控制数字孪生体运动。为支持稳健训练与公平评估，我们构建了一个大规模数据集——压力与惯性传感人类运动与交互数据集（PRISM），同步记录多种人体运动下的IMU与足底压力信息。实验表明，GRIP在所有测试数据集上均优于现有纯IMU及融合方法，显著提升全局姿态精度与物理一致性。
原文摘要 · Abstract (English)
We propose Ground Reaction Inertial Poser (GRIP), a method that reconstructs physically plausible human motion using four wearable devices. Unlike conventional IMU-only approaches, GRIP combines IMU signals with foot pressure data to capture both body dynamics and ground interactions. Furthermore, rather than relying solely on kinematic estimation, GRIP uses a digital twin of a person, in the form of a synthetic humanoid in a physics simulator, to reconstruct realistic and physically plausible motion. At its core, GRIP consists of two modules: KinematicsNet, which estimates body poses and velocities from sensor data, and DynamicsNet, which controls the humanoid in the simulator using the residual between the KinematicsNet prediction and the simulated humanoid state. To enable robust training and fair evaluation, we introduce a large-scale dataset, Pressure and Inertial Sensing for Human Motion and Interaction (PRISM), that captures diverse human motions with synchronized IMUs and insole pressure sensors. Experimental results show that GRIP outperforms existing IMU-only and IMU-pressure fusion methods across all evaluated datasets, achieving higher global pose accuracy and improved physical consistency.
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