Che-Chun Su

Papers

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About

Che-Chun Su is a leading researcher in computer vision, with a focus on human behavior understanding and autonomous systems. His work centers on monocular body orientation estimation, a critical challenge for applications like robotics and autonomous driving, where traditional 3D pose estimation often fails due to occlusion or low image resolution. Su’s most influential contribution is the MEBOW framework, introduced in his 2020 paper “MEBOW: Monocular Estimation of Body Orientation in the Wild,” which has garnered over 40 citations. This work pioneered the COCO-MEBOW dataset, enabling robust orientation estimation in real-world, unconstrained environments. By addressing a fundamental gap in visual perception—where even partial body cues can guide decision-making—Su’s research has direct implications for safer autonomous navigation and more intuitive human-robot interaction. His approach is particularly notable for its practicality, offering reliable performance where conventional methods struggle. With a growing citation impact, Su continues to advance the field, making his work essential reading for students and researchers interested in bridging the gap between low-level vision and high-level scene understanding.