服装惯性去噪器（GID）：通过松散IMU去噪实现准确的运动捕捉
Garment Inertial Denoiser (GID): Endowing Accurate Motion Capture via Loose IMU Denoiser
AI总结：
GID通过松散IMU去噪实现准确运动捕捉，利用位置感知专家架构和轻量级融合模块，提升惯性MoCap的准确性和泛化能力。
摘要：
可穿戴惯性运动捕捉（MoCap）提供了一种便携、遮挡自由且隐私保护的替代方案，但其准确性依赖于紧密贴合的传感器——这对日常使用来说是侵入性和不舒适的。将IMU嵌入松散穿着的服装中是一种理想的替代方案，但传感器与身体的位移引入了严重的、结构化的、位置依赖性的破坏，这会破坏标准惯性管道。我们提出了GID（服装惯性去噪器），一种轻量级、即插即用的Transformer，将松散穿着的MoCap分解为三个阶段：（i）位置特定的去噪，（ii）自适应跨穿着融合，（iii）通用姿态预测。GID使用一种位置感知的专家架构，其中共享的时空骨干模型全球运动，而每个IMU专家头专门处理局部服装动态，并且一个轻量级融合模块确保跨部分的一致性。这种归纳偏置使训练稳定，并有效学习有限的配对松紧IMU数据。我们还引入了GarMoCap，一个结合了公共和新收集数据集，涵盖多样用户、动作和服装的集。实验表明，GID能够从单用户训练实现准确、实时的去噪，并在未见过的用户、动作和服装类型上泛化，当用作即插模块时，能持续改进最先进的惯性MoCap方法。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
