52 citations · 53 across the 5 of their papers we have counts for
6 papers
Mojito: LLM-Aided Motion Instructor with Jitter-Reduced Inertial Tokens
Ziwei Shan, Yaoyu He, Chengfeng Zhao +5
Human bodily movements convey critical insights into action intentions and cognitive processes, yet existing multimodal systems primarily focused on understanding human motion via…
HOI-M3:Capture Multiple Humans and Objects Interaction within Contextual Environment
Juze Zhang, Jingyan Zhang, Zining Song +6
Humans naturally interact with both others and the surrounding multiple objects, engaging in various social activities. However, recent advances in modeling human-object interactio…
LiveHPS: LiDAR-based Scene-level Human Pose and Shape Estimation in Free Environment
Yiming Ren, Xiao Han, Chengfeng Zhao +4
For human-centric large-scale scenes, fine-grained modeling for 3D human global pose and shape is significant for scene understanding and can benefit many real-world applications.…
I'M HOI: Inertia-aware Monocular Capture of 3D Human-Object Interactions
Chengfeng Zhao, Juze Zhang, Jiashen Du +5
We are living in a world surrounded by diverse and "smart" devices with rich modalities of sensing ability. Conveniently capturing the interactions between us humans and these obje…
LiDAR-aid Inertial Poser: Large-scale Human Motion Capture by Sparse Inertial and LiDAR Sensors
Yiming Ren, Chengfeng Zhao, Yannan He +5
We propose a multi-sensor fusion method for capturing challenging 3D human motions with accurate consecutive local poses and global trajectories in large-scale scenarios, only usin…
HybridCap: Inertia-aid Monocular Capture of Challenging Human Motions
Han Liang, Yannan He, Chengfeng Zhao +4
Monocular 3D motion capture (mocap) is beneficial to many applications. The use of a single camera, however, often fails to handle occlusions of different body parts and hence it i…