Refining OpenPose with a new sports dataset for robust 2D pose estimation
Takumi Kitamura, Hitoshi Teshima, Diego Thomas, Hiroshi Kawasaki
Abstract
3D marker-less motion capture can be achieved by triangulating estimated multi-views 2D poses. However, when the 2D pose estimation fails, the 3D motion capture also fails. This is particularly challenging for sports performance of athletes, which have extreme poses. In extreme poses (like having the head down) state-of-the-art 2D pose estimator such as OpenPose do not work at all. In this paper, we propose a new method to improve the training of 2D pose estimators for extreme poses by leveraging a new sports dataset and our proposed data augmentation strategy. Our results show significant improvements over previous methods for 2D pose estimation of athletes performing acrobatic moves, while keeping state-of-the-art performance on standard datasets.