  Data labeling for learning 3D hand pose estimation models is a huge effort.
Readily available, accurately labeled synthetic data has the potential to
reduce the effort. However, to successfully exploit synthetic data, current
state-of-the-art methods still require a large amount of labeled real data. In
this work, we remove this requirement by learning to map from the features of
real data to the features of synthetic data mainly using a large amount of
synthetic and unlabeled real data. We exploit unlabeled data using two
auxiliary objectives, which enforce that (i) the mapped representation is pose
specific and (ii) at the same time, the distributions of real and synthetic
data are aligned. While pose specifity is enforced by a self-supervisory signal
requiring that the representation is predictive for the appearance from
different views, distributions are aligned by an adversarial term. In this way,
we can significantly improve the results of the baseline system, which does not
use unlabeled data and outperform many recent approaches already with about 1%
of the labeled real data. This presents a step towards faster deployment of
learning based hand pose estimation, making it accessible for a larger range of
applications.
