  When applying a pre-trained 2D-to-3D human pose lifting model to a target
unseen dataset, large performance degradation is commonly encountered due to
domain shift issues. We observe that the degradation is caused by two factors:
1) the large distribution gap over global positions of poses between the source
and target datasets due to variant camera parameters and settings, and 2) the
deficient diversity of local structures of poses in training. To this end, we
combine \textbf{global adaptation} and \textbf{local generalization} in
\textit{PoseDA}, a simple yet effective framework of unsupervised domain
adaptation for 3D human pose estimation. Specifically, global adaptation aims
to align global positions of poses from the source domain to the target domain
with a proposed global position alignment (GPA) module. And local
generalization is designed to enhance the diversity of 2D-3D pose mapping with
a local pose augmentation (LPA) module. These modules bring significant
performance improvement without introducing additional learnable parameters. In
addition, we propose local pose augmentation (LPA) to enhance the diversity of
3D poses following an adversarial training scheme consisting of 1) a
augmentation generator that generates the parameters of pre-defined pose
transformations and 2) an anchor discriminator to ensure the reality and
quality of the augmented data. Our approach can be applicable to almost all
2D-3D lifting models. \textit{PoseDA} achieves 61.3 mm of MPJPE on MPI-INF-3DHP
under a cross-dataset evaluation setup, improving upon the previous
state-of-the-art method by 10.2\%.
