  Most 3d human pose estimation methods assume that input -- be it images of a
scene collected from one or several viewpoints, or from a video -- is given.
Consequently, they focus on estimates leveraging prior knowledge and
measurement by fusing information spatially and/or temporally, whenever
available. In this paper we address the problem of an active observer with
freedom to move and explore the scene spatially -- in `time-freeze' mode --
and/or temporally, by selecting informative viewpoints that improve its
estimation accuracy. Towards this end, we introduce Pose-DRL, a fully trainable
deep reinforcement learning-based active pose estimation architecture which
learns to select appropriate views, in space and time, to feed an underlying
monocular pose estimator. We evaluate our model using single- and multi-target
estimators with strong result in both settings. Our system further learns
automatic stopping conditions in time and transition functions to the next
temporal processing step in videos. In extensive experiments with the Panoptic
multi-view setup, and for complex scenes containing multiple people, we show
that our model learns to select viewpoints that yield significantly more
accurate pose estimates compared to strong multi-view baselines.
