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.

翻译：基于惯性测量单元的方法为基于摄像头的动作捕捉提供了一种可穿戴式替代方案。为减轻惯性信号漂移，近期稀疏惯性姿态估计方法通过集成超宽带测距得到的传感器间距离进行补偿。目前，超宽带距离仅被用作额外输入特征，忽略了其对传感器位置施加的物理约束。然而，这些距离同样可用于重建底层三维传感器布局，从而为姿态重建提供更具信息量的输入。本文提出Ultra Diffusion Poser——一种显式建模这些几何约束的扩散模型。该模型包含空间布局模块，可通过超宽带测量值解析重建三维传感器位置。这些传感器位置与惯性测量单元信号及超宽带距离共同作为扩散过程中的条件信号使用。然而，网络预测可能违反传感器间测距约束。为解决此问题，我们引入超宽带扩散引导机制，在扩散采样过程中促进预测姿态与测量距离之间的对齐。这些贡献使模型达到当前最优性能，较先前工作关节位置误差降低达22%。