Bilan Liu

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About

Bilan Liu is a leading researcher in computer vision, with a primary focus on human pose and body orientation estimation—critical technologies for robotics, autonomous driving, and human-computer interaction. Her most influential work, "MEBOW: Monocular Estimation of Body Orientation in the Wild" (2020, 40 citations), tackles the challenging problem of inferring body orientation from single images, especially when traditional 3D pose estimation fails due to low resolution, occlusion, or ambiguous body parts. By introducing the COCO-MEBOW dataset, Liu provided a benchmark that has become essential for advancing orientation-aware vision systems. Her contributions are particularly impactful in real-world scenarios where robust visual cues are needed despite poor image quality. Liu’s research bridges the gap between theoretical computer vision and practical deployment, earning recognition for its direct applicability in autonomous navigation and surveillance. With a growing citation record, she continues to shape how machines understand human motion in unconstrained environments, making her work indispensable for students and engineers developing next-generation perception systems.