1 code implementation • 12 Jun 2025 • Chengxu Zuo, Jiawei Huang, Xiao Jiang, Yuan YAO, Xiangren Shi, Rui Cao, Xinyu Yi, Feng Xu, Shihui Guo, Yipeng Qin
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 IMU calibration, i. e., the coordinate drift RG'G and measurement offset RBS remain constant during the entire motion, thereby significantly expanding their application scenarios.
1 code implementation • 4 May 2024 • Haoyu Hu, Xinyu Yi, Zhe Cao, Jun-Hai Yong, Feng Xu
Hand manipulating objects is an important interaction motion in our daily activities.
1 code implementation • CVPR 2024 • Chengxu Zuo, Yiming Wang, Lishuang Zhan, Shihui Guo, Xinyu Yi, Feng Xu, Yipeng Qin
Existing wearable motion capture methods typically demand tight on-body fixation (often using straps) for reliable sensing limiting their application in everyday life.
1 code implementation • 1 Sep 2023 • Shaohua Pan, Qi Ma, Xinyu Yi, Weifeng Hu, Xiong Wang, Xingkang Zhou, Jijunnan Li, Feng Xu
We believe that the combination is complementary and able to solve the inherent difficulties of using one modality input, including occlusions, extreme lighting/texture, and out-of-view for visual mocap and global drifts for inertial mocap.
Ranked #1 on
3D Human Pose Estimation
on AIST++
no code implementations • 2 May 2023 • Xinyu Yi, Yuxiao Zhou, Marc Habermann, Vladislav Golyanik, Shaohua Pan, Christian Theobalt, Feng Xu
We integrate the two techniques together in EgoLocate, a system that simultaneously performs human motion capture (mocap), localization, and mapping in real time from sparse body-mounted sensors, including 6 inertial measurement units (IMUs) and a monocular phone camera.
1 code implementation • 22 Sep 2022 • Haoyu Hu, Xinyu Yi, Hao Zhang, Jun-Hai Yong, Feng Xu
Single view-based reconstruction of hand-object interaction is challenging due to the severe observation missing caused by occlusions.
1 code implementation • 10 May 2021 • Xinyu Yi, Yuxiao Zhou, Feng Xu
For global translation estimation, we propose a supporting-foot-based method and an RNN-based method to robustly solve for the global translations with a confidence-based fusion technique.