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We propose a Bayesian approximation to a deep learning architecture for 3Dhand pose estimation. Through this framework, we explore and analyse the twotypes of uncertainties that are influenced either by data or by the learningcapability. Furthermore, we draw comparisons against the standard estimatorover three popular benchmarks. The first contribution lies in outperforming thebaseline while in the second part we address the active learning application.We also show that with a newly proposed acquisition function, our Bayesian 3Dhand pose estimator obtains lowest errors with the least amount of data. Theunderlying code is publicly available athttps://github.com/razvancaramalau/al_bhpe.

Active Learning for Bayesian 3D Hand Pose Estimation