Improving 2D Human Pose Estimation across Unseen Camera Views with
  Synthetic Data
Miroslav Purkrábek Jiří Matas
Human Pose Estimation is a thoroughly researched problem; however, most
datasets focus on the side and front-view scenarios. We address the limitation
by proposing a novel approach that tackles the challenges posed by extreme
viewpoints and poses. We introduce a new method for synthetic data generation -
RePoGen, RarE POses GENerator - with comprehensive control over pose and view
to augment the COCO dataset. Experiments on a new dataset of real images show
that adding RePoGen data to the COCO surpasses previous attempts to top-view
pose estimation and significantly improves performance on the bottom-view
dataset. Through an extensive ablation study on both the top and bottom view
data, we elucidate the contributions of methodological choices and demonstrate
improved performance. The code and the datasets are available on the project
website.