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