Lingjie Liu

Papers

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About

Lingjie Liu is a computer vision and human-computer interaction researcher whose work bridges 3D scene understanding, human motion analysis, and intelligent perception systems. Among his most recognized contributions is his development of Epipolar Spatio-Temporal Networks for multi-view depth estimation from single videos — a method that advances how machines perceive and reconstruct three-dimensional environments, with direct applications in robotics, autonomous navigation, and augmented reality. This work has accumulated over 55 citations, reflecting its strong influence within the depth estimation community. Liu has also made meaningful strides in understanding human-object interactions, particularly through his 2022 research on predicting how humans manipulate large-sized objects from observed motion sequences. This work, garnering 21 citations, has implications for human-robot collaboration, virtual reality design, and behavioral surveillance systems. Across his portfolio, Liu demonstrates a consistent focus on equipping machines with the ability to interpret complex spatial and human behavioral data. His research combines deep learning architectures with geometric reasoning, making him a notable contributor to the evolving intersection of 3D vision and embodied intelligence.