Motion capture from a limited number of inertial measurement units (IMUs) has important applications in health, human performance, and virtual reality. Real-world limitations and application-specific goals dictate different IMU configurations (i.e., number of IMUs and chosen attachment body segments), trading off accuracy and practicality. Although recent works were successful in accurately reconstructing whole-body motion from six IMUs, these systems only work with a specific IMU configuration. Here we propose a single diffusion generative model, Diffusion Inertial Poser (DiffIP), which reconstructs human motion in real-time from arbitrary IMU configurations. We show that DiffIP has the benefit of flexibility with respect to the IMU configuration while being as accurate as the state-of-the-art for the commonly used six IMU configuration. Our system enables selecting an optimal configuration for different applications without retraining the model. For example, when only four IMUs are available, DiffIP found that the configuration that minimizes errors in joint kinematics instruments the thighs and forearms. However, global translation reconstruction is better when instrumenting the feet instead of the thighs. Although our approach is agnostic to the underlying model, we built DiffIP based on physiologically realistic musculoskeletal models to enable use in biomedical research and health applications.

翻译：通过有限数量的惯性测量单元（IMU）进行运动捕捉在健康、人体表现和虚拟现实领域具有重要应用。现实世界的限制和特定应用目标决定了不同的IMU配置（即IMU数量及所选择的附着身体部位），这需要在精度与实用性之间进行权衡。尽管近期研究成功利用六枚IMU实现了全身运动的精确重建，但这些系统仅适用于特定配置。本文提出了一种单一扩散生成模型——扩散惯性姿态器（DiffIP），能够从任意IMU配置中实时重建人体运动。我们证明，DiffIP在保持对IMU配置灵活性的同时，对于常用的六枚IMU配置，其精度可与当前最优方法相媲美。我们的系统无需重新训练模型即可为不同应用选择最优配置。例如，当仅有四枚IMU可用时，DiffIP发现能最小化关节运动学误差的配置是将IMU附着于大腿和前臂；然而，若优先考虑全局平移重建，将IMU附着于脚部而非大腿效果更佳。尽管我们的方法与底层模型无关，但DiffIP基于生理学上真实的肌肉骨骼模型构建，从而支持生物医学研究与健康应用。