  While pose estimation is an important computer vision task, it requires
expensive annotation and suffers from domain shift. In this paper, we
investigate the problem of domain adaptive 2D pose estimation that transfers
knowledge learned on a synthetic source domain to a target domain without
supervision. While several domain adaptive pose estimation models have been
proposed recently, they are not generic but only focus on either human pose or
animal pose estimation, and thus their effectiveness is somewhat limited to
specific scenarios. In this work, we propose a unified framework that
generalizes well on various domain adaptive pose estimation problems. We
propose to align representations using both input-level and output-level cues
(pixels and pose labels, respectively), which facilitates the knowledge
transfer from the source domain to the unlabeled target domain. Our experiments
show that our method achieves state-of-the-art performance under various domain
shifts. Our method outperforms existing baselines on human pose estimation by
up to 4.5 percent points (pp), hand pose estimation by up to 7.4 pp, and animal
pose estimation by up to 4.8 pp for dogs and 3.3 pp for sheep. These results
suggest that our method is able to mitigate domain shift on diverse tasks and
even unseen domains and objects (e.g., trained on horse and tested on dog). Our
code will be publicly available at:
https://github.com/VisionLearningGroup/UDA_PoseEstimation.
