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.

翻译：人体姿态估计是一个已被充分研究的问题，然而大多数数据集主要聚焦于侧视角和前视角场景。我们通过提出一种创新方法来解决极端视角和姿态带来的挑战。我们引入了一种新的合成数据生成方法——RePoGen（罕见姿态生成器），该方法可对姿态和视角进行全面控制，以增强COCO数据集。在真实图像新数据集上的实验表明，将RePoGen数据添加到COCO数据中，在顶部视角姿态估计任务上超越了以往方法，并显著提升了底部视角数据集的性能。通过对顶部和底部视角数据进行全面的消融研究，我们阐明了方法选择带来的贡献，并展示了性能提升的效果。相关代码和数据集已发布于项目网站。