  Pose estimation of the human body and hands is a fundamental problem in
computer vision, and learning-based solutions require a large amount of
annotated data. In this work, we improve the efficiency of the data annotation
process for 3D pose estimation problems with Active Learning (AL) in a
multi-view setting. AL selects examples with the highest value to annotate
under limited annotation budgets (time and cost), but choosing the selection
strategy is often nontrivial. We present a framework to efficiently extend
existing single-view AL strategies. We then propose two novel AL strategies
that make full use of multi-view geometry. Moreover, we demonstrate additional
performance gains by incorporating pseudo-labels computed during the AL
process, which is a form of self-training. Our system significantly outperforms
simulated annotation baselines in 3D body and hand pose estimation on two
large-scale benchmarks: CMU Panoptic Studio and InterHand2.6M. Notably, on CMU
Panoptic Studio, we are able to reduce the turn-around time by 60% and
annotation cost by 80% when compared to the conventional annotation process.
