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About

Jean-Marc Odobez is a prominent researcher specializing in human-robot interaction (HRI), multimodal perception, and social signal processing. His work sits at the intersection of computer vision, machine learning, and robotics, with a particular focus on enabling robots to perceive and respond naturally to human behavior in real-world environments. Odobez has made significant contributions to sound source localization, pioneering the use of deep neural networks for detecting and localizing multiple speakers simultaneously — work that has garnered over 200 citations and challenged conventional signal processing assumptions. His research on gaze estimation using RGB-D sensors and visual focus of attention recognition has advanced how robots understand human attention and conversational intent, critical capabilities for socially aware machines. His involvement in the MuMMER Project demonstrates a commitment to deploying HRI systems in authentic public spaces, moving beyond laboratory constraints. Foundational datasets like the Vernissage Corpus have provided the research community with valuable benchmarks for conversational HRI studies. His explorations of head pose, nod detection, and engagement-based multi-party dialogue further illustrate his holistic approach to modeling nonverbal communication. With hundreds of citations across diverse topics, Odobez's research has meaningfully shaped how modern robots perceive, interpret, and engage with humans in complex social settings.