Hanbyul Joo

Korea Advanced Institute of Science and Technology, Meta (Israel)

Papers

Total Citations

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About

Hanbyul Joo is a leading researcher in computer vision and robotics, with a focus on human pose estimation, shape matching, and autonomous navigation. His early work laid the foundation for robust object recognition in cluttered environments, as demonstrated in his 2009 paper on graph-based shape matching for robotic applications (13 citations). That same year, he pioneered non-contact terrain classification for autonomous mobile robots (11 citations), introducing vision-based methods to predict friction coefficients—critical for safe traversal in unstructured environments. More recently, Joo has made significant strides in egocentric vision with his 2020 work "You2Me" (3 citations), which tackles the challenging problem of inferring a camera wearer's 3D body pose from first-person video. By leveraging interactions between first and second persons, this learning-based approach opens new possibilities for augmented reality, healthcare, and robotics. Joo's contributions bridge classical shape analysis and modern deep learning, earning him recognition for advancing both theoretical understanding and practical applications in human-centered computing.