  In this paper, we are interested in pose estimation of animals. Animals
usually exhibit a wide range of variations on poses and there is no available
animal pose dataset for training and testing. To address this problem, we build
an animal pose dataset to facilitate training and evaluation. Considering the
heavy labor needed to label dataset and it is impossible to label data for all
concerned animal species, we, therefore, proposed a novel cross-domain
adaptation method to transform the animal pose knowledge from labeled animal
classes to unlabeled animal classes. We use the modest animal pose dataset to
adapt learned knowledge to multiple animals species. Moreover, humans also
share skeleton similarities with some animals (especially four-footed mammals).
Therefore, the easily available human pose dataset, which is of a much larger
scale than our labeled animal dataset, provides important prior knowledge to
boost up the performance on animal pose estimation. Experiments show that our
proposed method leverages these pieces of prior knowledge well and achieves
convincing results on animal pose estimation.
