IMU-HOI：通过接触感知惯性融合实现连贯人-物交互与运动捕捉的共生框架
IMU-HOI: A Symbiotic Framework for Coherent Human-Object Interaction and Motion Capture via Contact-Conscious Inertial Fusion
把稀疏 IMU 信号与手-物接触概率作为高层路由信号，融合运动学与惯性积分，可在遮挡严重、相机视野受限的场景下替代纯视觉动捕；其接触感知的三阶段融合管线与可插拔设计，可直接接入现有稀疏 IMU 动捕骨干，扩展到人-物联合运动估计。
原文依据 infers probabilistic hand-object contacts directly from IMU streams and uses them as a high-level signal to route between kinematic and inertial reasoning
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论捕捉包含物体交互的全身人体运动对 AR/VR 与机器人应用至关重要，然而由于遮挡与受限的捕捉范围，传统基于视觉的方法仍面临挑战。惯性测量单元（IMU）提供了一种无需视线约束的有力替代方案，但现有的基于 IMU 的运动捕捉假设人体是孤立的，忽略了物体接触与动力学。为弥补这一空白，我们提出 IMU-HOI，一种新颖的框架，从身体与物体上的稀疏 IMU 联合恢复全身人体姿态与 6-DoF 物体轨迹，并显式建模人-物交互。我们的方法首先直接从 IMU 流中推断手-物接触的概率，并将其作为高层信号在运动学推理与惯性推理之间进行路由。这些接触线索驱动一个三阶段融合管线，对人体姿态与根节点平移进行细化，并将基于手部的前向运动学与物体 IMU 积分相融合以得到物体运动，从而为人体与物体产生连贯且抗漂移的轨迹。在具有挑战性的人-物交互场景上的实验表明，相较于先前的惯性运动捕捉方法，精度获得显著提升。此外，IMU-HOI 可以以极小的改动即插即用地接入现有的稀疏 IMU 动捕骨干，有效将纯惯性运动捕捉的范围从孤立人体扩展到完整的人-物交互与联合运动估计。
英文原文摘要 1471 字符
Capturing full-body human motion with object interactions is crucial for AR/VR and robotics applications, yet it remains challenging for conventional vision-based methods due to occlusions and constrained capture volumes. Inertial measurement units (IMUs) offer a compelling alternative without line-of-sight requirements, but existing IMU-based motion capture assumes an isolated human and ignores object contacts and dynamics. To bridge this gap, we present IMU-HOI, a novel framework that jointly recovers full-body human pose and 6-DoF object trajectory from sparse IMUs on the body and object, explicitly modeling human-object Interaction.Our approach first infers probabilistic hand-object contacts directly from IMU streams and uses them as a high-level signal to route between kinematic and inertial reasoning. These contact cues drive a three-stage fusion pipeline that refines human pose and root translation, and fuses hand-based forward kinematics with object-IMU integration for object motion, yielding coherent, drift-resilient trajectories for both human and object. Experiments on challenging human-object interaction scenarios demonstrate substantial accuracy gains over prior inertial motion capture methods. Moreover, IMU-HOI can be plugged into existing sparse-IMU mocap backbones with minimal changes, effectively extending the scope of purely inertial motion capture from isolated humans to full human-object interaction and joint motion estimation.