3 papers
cs.GR2026
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…
cs.CV2026
Garment Inertial Denoiser (GID): Endowing Accurate Motion Capture via Loose IMU Denoiser
Jiawei Fang, Ruonan Zheng, Xiaoxia Gao +4
Wearable inertial motion capture (MoCap) provides a portable, occlusion-free, and privacy-preserving alternative to camera-based systems, but its accuracy depends on tightly attach…
cs.GR2025
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…