Qing Shuai
Papers
1
Total Citations
52
H-Index
1
About
Qing Shuai is a leading researcher in computer vision, with a primary focus on monocular 3D human pose estimation—the challenging task of reconstructing three-dimensional human body positions from single-camera RGB images and videos. Their most cited work, a comprehensive 2020 survey on the topic, has garnered 52 citations and serves as a foundational reference for the field. In this survey, Shuai systematically reviews the evolution of methods, from classical optimization-based approaches to modern deep learning techniques, highlighting key innovations in handling occlusions, depth ambiguity, and real-time performance. Beyond this survey, Shuai’s contributions have advanced applications in human-computer interaction, robotics, video analytics, and augmented reality, where accurate pose recovery from minimal sensor input is critical. Their work is particularly notable for bridging the gap between theoretical models and practical, sensor-light deployments, making 3D pose estimation more accessible for real-world systems. With a growing citation impact, Shuai continues to shape how machines perceive and interpret human motion, offering valuable insights for students and researchers aiming to push the boundaries of visual understanding.
Research Focus
Key Achievements
Top Papers
- 1A survey on monocular 3D human pose estimation52 citations · 2020