Ultra Diffusion Poser：基于扩散模型从稀疏惯性传感器与测距传感器间距离进行人体运动跟踪
Ultra Diffusion Poser: Diffusion-Based Human Motion Tracking from Sparse Inertial Sensors and Ranging-based Between-sensor Distances
把 UWB 测距的传感器间距离从「额外输入特征」升级为可解析重建 3D 传感器布局的几何约束，并用扩散采样期的 guidance 强制预测姿态与实测距离对齐——这套「解析几何重建 + 采样期约束引导」思路可迁移到任何带物理距离/结构约束的稀疏传感或稀疏视角姿态重建任务。
原文依据 UWB-Diffusion Guidance, which encourages alignment between predicted poses and measured distances during diffusion sampling
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论使用惯性测量单元（IMU）的方法为基于相机的动作捕捉提供了一种可穿戴替代方案。为缓解惯性信号带来的漂移，近期的稀疏惯性姿态估计器引入了由超宽带（UWB）测距测得的传感器间距离。迄今为止，UWB 距离仅被用作额外的输入特征，忽略了其对传感器位置施加的物理约束。然而，这些距离也可用于重建底层的三维传感器布局，进而为姿态重建提供信息更丰富的输入。我们提出 Ultra Diffusion Poser，一个显式建模这些几何约束的扩散模型。它包含一个空间布局模块，可从 UWB 测量中解析地重建三维传感器位置。这些传感器位置与 IMU 信号和 UWB 距离一起，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传感器间距离测量。为解决这一问题，我们引入 UWB-Diffusion Guidance，在扩散采样过程中促使预测姿态与实测距离之间保持对齐。这些贡献共同使我们的模型达到最优性能，相比先前工作将关节位置误差最多降低 22%。代码见 https://github.com/eth-siplab/UltraDiffusionPoser。
英文原文摘要 1352 字符
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. Code can be found here https://github.com/eth-siplab/UltraDiffusionPoser.