  Monocular estimation of 3d human pose has attracted increased attention with
the availability of large ground-truth motion capture datasets. However, the
diversity of training data available is limited and it is not clear to what
extent methods generalize outside the specific datasets they are trained on. In
this work we carry out a systematic study of the diversity and biases present
in specific datasets and its effect on cross-dataset generalization across a
compendium of 5 pose datasets. We specifically focus on systematic differences
in the distribution of camera viewpoints relative to a body-centered coordinate
frame. Based on this observation, we propose an auxiliary task of predicting
the camera viewpoint in addition to pose. We find that models trained to
jointly predict viewpoint and pose systematically show significantly improved
cross-dataset generalization.
