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About

Federica Bogo is a leading researcher at the intersection of computer vision, mixed reality, and human body understanding, with her work spanning 3D human motion capture, spatial computing, and egocentric sensing. Her research addresses some of the most challenging problems in recovering realistic human body shape and motion from visual data, particularly in complex, real-world scenes. Among her most influential contributions is her work on 4D human body capture in 3D scenes, where she pioneered the use of learned motion priors to recover high-quality human motion from monocular video — a notoriously difficult problem complicated by occlusions and partial views, with significant implications for AR/VR and robotics. Her EgoBody dataset and framework further advanced the field by enabling human body shape and motion estimation from head-mounted devices during social interactions. Bogo has also made foundational contributions to mixed reality research, most notably through HoloLens 2 Research Mode (118 citations), which opened Microsoft's flagship headset as a platform for the broader computer vision community. Her work on spatial computing and human-robot interaction further demonstrates her commitment to bridging cutting-edge research with real-world application, making her a pivotal figure in embodied AI and immersive computing research.