1 citations · 1 across the 5 of their papers we have counts for
5 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…
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…
SuDA: Support-based Domain Adaptation for Sim2Real Motion Capture with Flexible Sensors
Jiawei Fang, Haishan Song, Chengxu Zuo +4
Flexible sensors hold promise for human motion capture (MoCap), offering advantages such as wearability, privacy preservation, and minimal constraints on natural movement. However,…
Computational Design of Wiring Layout on Tight Suits with Minimal Motion Resistance
Kai Wang, Xiaoyu Xu, Yinping Zhen +4
An increasing number of electronics are directly embedded on the clothing to monitor human status (e.g., skeletal motion) or provide haptic feedback. A specific challenge to protot…