Wearable inertial motion capture (MoCap) provides a portable, occlusion-free, and privacy-preserving alternative to camera-based systems, but its accuracy depends on tightly attached sensors - an intrusive and uncomfortable requirement for daily use. Embedding IMUs into loose-fitting garments is a desirable alternative, yet sensor-body displacement introduces severe, structured, and location-dependent corruption that breaks standard inertial pipelines. We propose GID (Garment Inertial Denoiser), a lightweight, plug-and-play Transformer that factorizes loose-wear MoCap into three stages: (i) location-specific denoising, (ii) adaptive cross-wear fusion, and (iii) general pose prediction. GID uses a location-aware expert architecture, where a shared spatio-temporal backbone models global motion while per-IMU expert heads specialize in local garment dynamics, and a lightweight fusion module ensures cross-part consistency. This inductive bias enables stable training and effective learning from limited paired loose-tight IMU data. We also introduce GarMoCap, a combined public and newly collected dataset covering diverse users, motions, and garments. Experiments show that GID enables accurate, real-time denoising from single-user training and generalizes across unseen users, motions, and garment types, consistently improving state-of-the-art inertial MoCap methods when used as a drop-in module.

翻译：可穿戴惯性运动捕捉系统为基于摄像头的系统提供了一种便携、无遮挡且保护隐私的替代方案，但其精度依赖于紧密贴附的传感器——这对日常使用而言是一种侵入式且不舒适的要求。将惯性测量单元嵌入宽松服装是一种理想的替代方案，然而传感器与身体之间的相对位移会引入严重、结构化且位置相关的干扰，从而破坏标准惯性处理流程。我们提出GID（服装惯性去噪器），一种轻量级即插即用Transformer架构，将宽松穿着条件下的运动捕捉分解为三个阶段：（i）位置特异性去噪，（ii）自适应跨穿着部位融合，以及（iii）通用姿态预测。GID采用位置感知专家架构，其中共享的时空主干网络建模全局运动，而每个IMU的专家头部专门处理局部服装动力学，轻量级融合模块确保跨部位一致性。这种归纳偏置实现了稳定训练，并能从有限的成对宽松-紧身IMU数据中进行有效学习。我们还推出了GarMoCap数据集，该数据集结合了公开数据与新采集数据，涵盖多样化用户、动作与服装类型。实验表明，GID能够通过单用户训练实现精准的实时去噪，并在未见过的用户、动作和服装类型上展现泛化能力；当作为即插即用模块使用时，能持续提升当前最先进的惯性运动捕捉方法的性能。