101 citations · 184 across the 13 of their papers we have counts for
6 papers · 1 filter
Shape-aware Inertial Poser: Motion Tracking for Humans with Diverse Shapes Using Sparse Inertial Sensors
Lu Yin, Ziying Shi, Yinghao Wu +3
Human motion capture with sparse inertial sensors has gained significant attention recently. However, existing methods almost exclusively rely on a template adult body shape to mod…
Transformer IMU Calibrator: Dynamic On-body IMU Calibration for Inertial Motion Capture
Chengxu Zuo, Jiawei Huang, Xiao Jiang +7
In this paper, we propose a novel dynamic calibration method for sparse inertial motion capture systems, which is the first to break the restrictive absolute static assumption in I…
Improving Global Motion Estimation in Sparse IMU-based Motion Capture with Physics
Xinyu Yi, Shaohua Pan, Feng Xu
By learning human motion priors, motion capture can be achieved by 6 inertial measurement units (IMUs) in recent years with the development of deep learning techniques, even though…
Physical Non-inertial Poser (PNP): Modeling Non-inertial Effects in Sparse-inertial Human Motion Capture
Xinyu Yi, Yuxiao Zhou, Feng Xu
Existing inertial motion capture techniques use the human root coordinate frame to estimate local poses and treat it as an inertial frame by default. We argue that when the root ha…
Physical Inertial Poser (PIP): Physics-aware Real-time Human Motion Tracking from Sparse Inertial Sensors
Xinyu Yi, Yuxiao Zhou, Marc Habermann +4
Motion capture from sparse inertial sensors has shown great potential compared to image-based approaches since occlusions do not lead to a reduced tracking quality and the recordin…
TransPose: Real-time 3D Human Translation and Pose Estimation with Six Inertial Sensors
Xinyu Yi, Yuxiao Zhou, Feng Xu
Motion capture is facing some new possibilities brought by the inertial sensing technologies which do not suffer from occlusion or wide-range recordings as vision-based solutions d…