I’M HOI: Inertia-aware Monocular Capture of 3D Human-Object Interactions^🔗

IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2024.

Authors: Chengfeng Zhao, Juze Zhang, Jiashen Du, Ziwei Shan, Junye Wang, Jingyi Yu, Jingya Wang, Lan Xu

Abstract: We present I’m-HOI, a monocular scheme to faithfully capture the 3D motions of both the human and object in a novel setting: using a minimal amount of RGB camera and object-mounted Inertial Measurement Unit (IMU).

HOI-M$^3$: Capture Multiple Humans and Objects Interaction within Contextual Environment^🔗

IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2024.

Authors: Juze Zhang*, Jingyan Zhang*, Zining Song, Zhanhe Shi, Chengfeng Zhao, Ye Shi, Jingyi Yu, Lan Xu, Jingya Wang

Abstract: In this paper, we introduce HOI-M$^3$, a novel large-scale dataset for modeling the interactions of Multiple huMans and Multiple objects.

LiveHPS: LiDAR-based Scene-level Human Pose and Shape Estimation in Free Environment^🔗

IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2024.

Authors: Yiming Ren, Xiao Han, Chengfeng Zhao, Jingya Wang, Lan Xu, Jingyi Yu, Yuexin Ma

Abstract: In this paper, we present LiveHPS, a novel single LiDAR-based approach for scene-level Human Pose and Shape estimation without any limitation of light conditions and wearable devices.

LiDAR-aid Inertial Poser: Large-scale Human Motion Capture by Sparse Inertial and LiDAR Sensors^🔗

IEEE Transactions on Visualization and Computer Graphics (Proc. IEEE VR), 2023.

Authors: Yiming Ren*, Chengfeng Zhao*, Yannan He, Peishan Cong, Han Liang, Jingyi Yu, Lan Xu, Yuexin Ma

• * represents equal contribution.

Abstract: We propose a multi-sensor fusion method for capturing challenging 3D human motions using single LiDAR and 4 IMUs.

HybridCap: Inertia-aid Monocular Capture of Challenging Human Motions^🔗

The 37th AAAI Conference on Artificial Intelligence (AAAI), 2023.

Authors: Han Liang, Yannan He, Chengfeng Zhao, Mutian Li, Jingya Wang, Jingyi Yu, Lan Xu

Abstract: We present a light-weight, hybrid mocap technique called HybridCap that augments the camera with only 4 Inertial Measurement Units (IMUs) in a learning-and-optimization framework.