Improving 2D Human Pose Estimation in Rare Camera Views with Synthetic Data

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 the project website 1 .