用扩散模型融合惯性传感器与超宽带测距，提升人体动作捕捉精度。
Ultra Diffusion Poser: Diffusion-Based Human Motion Tracking From Sparse Inertial Sensors and Ranging-Based Between-Sensor Distances
- 通过超宽带测距重建传感器三维位置，作为扩散模型的几何约束条件。
- 在真实数据上将关节定位误差降低22%，优于现有方法。
- 适合需要高精度可穿戴动作捕捉的应用场景，如虚拟现实。
基于惯性测量单元（IMUs）的方法提供了可穿戴式动作捕捉的替代方案。为缓解惯性信号漂移问题，近期稀疏惯性姿态估计器引入了由超宽带（UWB）测距获得的传感器间距离。然而，以往工作仅将这些距离作为额外输入特征，忽略了其对传感器位置的物理约束。实际上，这些距离可用于重建原始3D传感器布局，从而为姿态重建提供更丰富的输入。我们提出Ultra Diffusion Poser，一种显式建模此类几何约束的扩散模型。其包含一个空间布局模块，能从UWB测量值中解析重建3D传感器位置。这些位置与IMU信号及UWB距离共同作为扩散过程中的条件信号。尽管如此，网络预测仍可能违反实测距离。为此，我们引入UWB-Diffusion Guidance，引导采样过程中预测姿态与实测距离保持一致。两项贡献结合使模型达到当前最优性能，在真实数据上将关节位置误差降低最多达22%。
原文摘要 · Abstract (English)
Methods using inertial measurement units (IMUs) provide a wearable alternative to camera-based motion capture. To mitigate drift from inertial signals, recent sparse inertial pose estimators integrate inter-sensor distances measured by ultra-wideband (UWB) ranging. So far, UWB distances have only been used as an additional input feature, ignoring the physical constraints they impose on sensor positions. However, these distances can also be used to reconstruct the underlying 3D sensor layout, which in turn provides more informative input for pose reconstruction. We propose Ultra Diffusion Poser, a diffusion model that explicitly models these geometric constraints. It includes a Spatial Layout Module that analytically reconstructs the 3D sensor positions from UWB measurements. These sensor positions are used alongside IMU signals and UWB distances as a conditioning signal during diffusion. Still, network predictions can violate inter-sensor distance measurements. To address this, we introduce UWB-Diffusion Guidance, which encourages alignment between predicted poses and measured distances during diffusion sampling. Together, these contributions enable our model to achieve state-of-the-art performance, reducing joint position error by up to 22% over prior work.
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