In this paper, we investigate a synthetic data sampling approach towards unsupervised domain adaptation (UDA) for pose estimation. UDA is characterized by a labeled source domain and an unlabeled target domain. We observe that recent work in UDA for pose estimation fails to generalize across poses in target data, despite having support for such poses in the source data. We hypothesize that this failure to generalize is due to a lack of uniform support across poses of varying complexity in the source domain. Motivated by this challenge, we aim to sample and train with the source domain data to improve the domain adaptation performance on a target domain. The proposed sampling strategy sorts the source domain samples based on a difficulty score, which reflects the lack of uniform support across varying pose complexity in the source domain. The difficulty score is a reconstruction error obtained from training an auto-encoder on the source domain poses. We categorize the dataset into closely related groups using this score. Selectively training from all or some of these groups help us to better utilize the source pose distribution. Finally, current pose estimation evaluation metrics do not effectively measure the ability of the model to learn the geometry of pose. We evaluate our approach qualitatively and quantitatively on benchmark datasets. Our sampling strategy outperforms existing state-of-the-art for domain adaptation.