101 citations · 184 across the 13 of their papers we have counts for
14 papers
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…
DiffCap: Diffusion-based Real-time Human Motion Capture using Sparse IMUs and a Monocular Camera
Shaohua Pan, Xinyu Yi, Yan Zhou +4
Combining sparse IMUs and a monocular camera is a new promising setting to perform real-time human motion capture. This paper proposes a diffusion-based solution to learn human mot…
BaroPoser: Real-time Human Motion Tracking from IMUs and Barometers in Everyday Devices
Libo Zhang, Xinyu Yi, Feng Xu
In recent years, tracking human motion using IMUs from everyday devices such as smartphones and smartwatches has gained increasing popularity. However, due to the sparsity of senso…
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…
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…
Hand-Object Interaction Controller (HOIC): Deep Reinforcement Learning for Reconstructing Interactions with Physics
Haoyu Hu, Xinyu Yi, Zhe Cao +2
Hand manipulating objects is an important interaction motion in our daily activities. We faithfully reconstruct this motion with a single RGBD camera by a novel deep reinforcement…
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…
Fusing Monocular Images and Sparse IMU Signals for Real-time Human Motion Capture
Shaohua Pan, Qi Ma, Xinyu Yi +5
Either RGB images or inertial signals have been used for the task of motion capture (mocap), but combining them together is a new and interesting topic. We believe that the combina…
EgoLocate: Real-time Motion Capture, Localization, and Mapping with Sparse Body-mounted Sensors
Xinyu Yi, Yuxiao Zhou, Marc Habermann +4
Human and environment sensing are two important topics in Computer Vision and Graphics. Human motion is often captured by inertial sensors, while the environment is mostly reconstr…
Physical Interaction: Reconstructing Hand-object Interactions with Physics
Haoyu Hu, Xinyu Yi, Hao Zhang +2
Single view-based reconstruction of hand-object interaction is challenging due to the severe observation missing caused by occlusions. This paper proposes a physics-based method to…
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…
StarNet: Targeted Computation for Object Detection in Point Clouds
Jiquan Ngiam, Benjamin Caine, Wei Han +10
Detecting objects from LiDAR point clouds is an important component of self-driving car technology as LiDAR provides high resolution spatial information. Previous work on point-clo…