  Deep learning approaches have been rapidly adopted across a wide range of
fields because of their accuracy and flexibility, but require large labeled
training datasets. This presents a fundamental problem for applications with
limited, expensive, or private data (i.e. small data), such as human pose and
behavior estimation/tracking which could be highly personalized. In this paper,
we present a semi-supervised data augmentation approach that can synthesize
large scale labeled training datasets using 3D graphical engines based on a
physically-valid low dimensional pose descriptor. To evaluate the performance
of our synthesized datasets in training deep learning-based models, we
generated a large synthetic human pose dataset, called ScanAva using 3D scans
of only 7 individuals based on our proposed augmentation approach. A
state-of-the-art human pose estimation deep learning model then was trained
from scratch using our ScanAva dataset and could achieve the pose estimation
accuracy of 91.2% at PCK0.5 criteria after applying an efficient domain
adaptation on the synthetic images, in which its pose estimation accuracy was
comparable to the same model trained on large scale pose data from real humans
such as MPII dataset and much higher than the model trained on other synthetic
human dataset such as SURREAL.
