10 citations · 26 across the 5 of their papers we have counts for
5 papers
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…
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 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…
Monocular Real-time Full Body Capture with Inter-part Correlations
Yuxiao Zhou, Marc Habermann, Ikhsanul Habibie +3
We present the first method for real-time full body capture that estimates shape and motion of body and hands together with a dynamic 3D face model from a single color image. Our a…
Monocular Real-time Hand Shape and Motion Capture using Multi-modal Data
Yuxiao Zhou, Marc Habermann, Weipeng Xu +3
We present a novel method for monocular hand shape and pose estimation at unprecedented runtime performance of 100fps and at state-of-the-art accuracy. This is enabled by a new lea…