Chen Qian

Group Sense (China), Wuhan University of Technology, Shanghai Jiao Tong University

Papers

Total Citations

134

H-Index

About

Chen Qian is a researcher working at the intersection of computer vision, robotics, and human-computer interaction, with a particular focus on advancing how machines perceive and respond to the human body. His most influential contribution, "3D Human Pose Machines with Self-supervised Learning" (2019), has garnered over 100 citations and addresses one of the field's most formidable challenges: accurately recovering three-dimensional human poses despite diverse appearances, occlusions, varying viewpoints, and inherent geometric ambiguities. By incorporating self-supervised learning, his approach significantly reduces reliance on costly labeled data, making robust pose estimation more accessible for real-world applications in robotics and computer vision. Beyond pose estimation, Qian has made notable strides in robotic calibration, with his 2023 work on matrix-solving hand-eye calibration accounting for robot kinematic errors earning 25 citations — a practical contribution that enhances precision in robotic manipulation. His earlier work on EEG-based affective recognition in human-robot interaction further demonstrates his broad commitment to building robots that are not only physically capable but also emotionally aware. Collectively, his research reflects a cohesive vision of intelligent, human-centered robotics systems.