Jinuk Heo

Papers

Total Citations

H-Index

About

Jinuk Heo is a leading researcher in human-robot interaction and motion tracking, with a focus on developing robust, real-time systems for hand motion capture and teleoperation. His most impactful work, "Visual-inertial hand motion tracking with robustness against occlusion, interference, and contact" (2021, 77 citations), addresses critical limitations in existing hand-tracking technologies. Heo's approach fuses visual and inertial sensors to maintain accurate tracking even under severe occlusions, electromagnetic interference, and physical contact—conditions that typically degrade performance. This contribution has significant implications for virtual reality, robotics, and assistive technologies. Heo also co-authored a comprehensive "Hand Tracking: Survey" (2024, 8 citations), consolidating the field's state-of-the-art. His recent work, "Human-in-the-Loop Gaussian Splatting for Robotic Teleoperation" (2025, 3 citations), introduces a novel 3D scene representation that provides operators with rich spatial context, moving beyond traditional camera streams to enable safer, more intuitive remote manipulation. By combining robust sensor fusion with advanced 3D visualization, Heo's research pushes the boundaries of how humans interact with machines in complex, real-world environments.