  Recent advancements in computer vision have seen a rise in the prominence of
applications using neural networks to understand human poses. However, while
accuracy has been steadily increasing on State-of-the-Art datasets, these
datasets often do not address the challenges seen in real-world applications.
These challenges are dealing with people distant from the camera, people in
crowds, and heavily occluded people. As a result, many real-world applications
have trained on data that does not reflect the data present in deployment,
leading to significant underperformance. This article presents ADG-Pose, a
method for automatically generating datasets for real-world human pose
estimation. These datasets can be customized to determine person distances,
crowdedness, and occlusion distributions. Models trained with our method are
able to perform in the presence of these challenges where those trained on
other datasets fail. Using ADG-Pose, end-to-end accuracy for real-world
skeleton-based action recognition sees a 20% increase on scenes with moderate
distance and occlusion levels, and a 4X increase on distant scenes where other
models failed to perform better than random.
