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2D/3D human pose estimation is needed to develop novel intelligent tools forthe operating room that can analyze and support the clinical activities. Thelack of annotated data and the complexity of state-of-the-art pose estimationapproaches limit, however, the deployment of such techniques inside the OR. Inthis work, we propose to use knowledge distillation in a teacher/studentframework to harness the knowledge present in a large-scale non-annotateddataset and in an accurate but complex multi-stage teacher network to train alightweight network for joint 2D/3D pose estimation. The teacher network alsoexploits the unlabeled data to generate both hard and soft labels useful inimproving the student predictions. The easily deployable network trained usingthis effective self-supervision strategy performs on par with the teachernetwork on \emph{MVOR+}, an extension of the public MVOR dataset where allpersons have been fully annotated, thus providing a viable solution forreal-time 2D/3D human pose estimation in the OR.

Self-supervision on Unlabelled OR Data for Multi-person 2D/3D Human Pose Estimation