Yuzhao Chen

Southeast University, Hebei University of Technology

Papers

Total Citations

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About

Yuzhao Chen is a rising researcher at the forefront of computer vision and human motion analysis, with a focus on bridging the gap between machine perception and real-world physical interaction. His work primarily spans two critical domains: coordinated movement classification and camera pose estimation. In his highly cited 2024 paper, "TAGL: Temporal-Guided Adaptive Graph Learning Network for Coordinated Movement Classification," Chen introduced a novel framework that leverages temporal dynamics and adaptive graph structures to decode complex human movements. This work, garnering 15 citations, has direct implications for robot-assisted rehabilitation, where understanding the nuanced interplay between the nervous system and muscles is essential. More recently, Chen tackled a fundamental challenge in computer vision with his 2025 paper on a "Generalized differentiable Perspective-n-Point without 2D-3D correspondences." By proposing a differentiable solution to the blind PnP problem—which traditionally suffers from extensive search spaces and outliers—he has opened new pathways for accurate camera pose measurement in unstructured environments. Though early in its citation lifecycle, this work signals Chen’s ambition to solve long-standing geometric estimation problems. His contributions are already shaping how machines perceive and interact with human motion and spatial environments.