4 papers
Distinguishing Imitation Error from Intrinsic Motion Learning Difficulty
Zhaorui Meng, Lu Yin, Xinrui Chen +4
Physics-based motion imitation is central to humanoid control, yet current evaluation metrics (e.g., MPJPE) only quantify imitation outcomes, not their underlying causes. This conf…
Improving Sparse IMU-based Motion Capture with Motion Label Smoothing
Zhaorui Meng, Lu Yin, Yangqing Hou +3
Sparse Inertial Measurement Units (IMUs) based human motion capture has gained significant momentum, driven by the adaptation of fundamental AI tools such as recurrent neural netwo…
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…
MagShield: Towards Better Robustness in Sparse Inertial Motion Capture Under Magnetic Disturbances
Yunzhe Shao, Xinyu Yi, Lu Yin +3
This paper proposes a novel method called MagShield, designed to address the issue of magnetic interference in sparse inertial motion capture (MoCap) systems. Existing Inertial Mea…