  Methods and datasets for human pose estimation focus predominantly on side-
and front-view scenarios. We overcome the limitation by leveraging synthetic
data and introduce RePoGen (RarE POses GENerator), an SMPL-based method for
generating synthetic humans with comprehensive control over pose and view.
Experiments on top-view datasets and a new dataset of real images with diverse
poses show that adding the RePoGen data to the COCO dataset outperforms
previous approaches to top- and bottom-view pose estimation without harming
performance on common views. An ablation study shows that anatomical
plausibility, a property prior research focused on, is not a prerequisite for
effective performance. The introduced dataset and the corresponding code are
available on https://mirapurkrabek.github.io/RePoGen-paper/ .
