It is especially challenging to achieve real-time human motion tracking on a
standalone VR Head-Mounted Display (HMD) such as Meta Quest and PICO. In this
paper, we propose HMD-Poser, the first unified approach to recover full-body
motions using scalable sparse observations from HMD and body-worn IMUs. In
particular, it can support a variety of input scenarios, such as HMD,
HMD+2IMUs, HMD+3IMUs, etc. The scalability of inputs may accommodate users'
choices for both high tracking accuracy and easy-to-wear. A lightweight
temporal-spatial feature learning network is proposed in HMD-Poser to guarantee
that the model runs in real-time on HMDs. Furthermore, HMD-Poser presents
online body shape estimation to improve the position accuracy of body joints.
Extensive experimental results on the challenging AMASS dataset show that
HMD-Poser achieves new state-of-the-art results in both accuracy and real-time
performance. We also build a new free-dancing motion dataset to evaluate
HMD-Poser's on-device performance and investigate the performance gap between
synthetic data and real-captured sensor data. Finally, we demonstrate our
HMD-Poser with a real-time Avatar-driving application on a commercial HMD. Our
code and free-dancing motion dataset are available
this https URL

使用 HMD-Poser 方法实现了基于 VR 头戴式显示器的人体实时运动追踪，通过 HMD 和穿戴式 IMUs 之间的观察数据，提高了追踪精确度和易于佩戴性。通过实验表明，HMD-Poser 在精确度和实时性方面达到了最新的最佳效果，并通过实时的 Avatar-driving 应用程序证明了其性能。

HMD-Poser：基于可扩展稀疏观测的设备端实时人体动作跟踪

HMD-Poser: On-Device Real-time Human Motion Tracking from Scalable  Sparse Observations

Smartphone applications designed to track human motion in combination with
wearable sensors, e.g., during physical exercising, raised huge attention
recently. Commonly, they provide quantitative services, such as personalized
training instructions or the counting of distances. But qualitative monitoring
and assessment is still missing, e.g., to detect malpositions, to prevent
injuries, or to optimize training success. We address this issue by presenting
a concept for qualitative as well as generic assessment of recurrent human
motion by processing multi-dimensional, continuous time series tracked with
motion sensors. Therefore, our segmentation procedure extracts individual
events of specific length and we propose expressive features to accomplish a
qualitative motion assessment by supervised classification. We verified our
approach within a comprehensive study encompassing 27 athletes undertaking
different body weight exercises. We are able to recognize six different
exercise types with a success rate of 100% and to assess them qualitatively
with an average success rate of 99.3%.

本文介绍了一种结合智能手机应用程序和可穿戴传感器的人体运动定量监测，旨在提高运动员安全性、预防受伤和优化训练效果的质性评估方法，基于时序序列数据进行个体特征提取，并通过监督分类技术的应用成功地评估了 27 名运动员的训练动作类型和质量。