  Domain adaptive pose estimation aims to enable deep models trained on source
domain (synthesized) datasets produce similar results on the target domain
(real-world) datasets. The existing methods have made significant progress by
conducting image-level or feature-level alignment. However, only aligning at a
single level is not sufficient to fully bridge the domain gap and achieve
excellent domain adaptive results. In this paper, we propose a multi-level
domain adaptation aproach, which aligns different domains at the image,
feature, and pose levels. Specifically, we first utilize image style transer to
ensure that images from the source and target domains have a similar
distribution. Subsequently, at the feature level, we employ adversarial
training to make the features from the source and target domains preserve
domain-invariant characeristics as much as possible. Finally, at the pose
level, a self-supervised approach is utilized to enable the model to learn
diverse knowledge, implicitly addressing the domain gap. Experimental results
demonstrate that significant imrovement can be achieved by the proposed
multi-level alignment method in pose estimation, which outperforms previous
state-of-the-art in human pose by up to 2.4% and animal pose estimation by up
to 3.1% for dogs and 1.4% for sheep.
