  We present a framework for diversifying human poses in a synthetic dataset
for aerial-view human detection. Our method firstly constructs a set of novel
poses using a pose generator and then alters images in the existing synthetic
dataset to assume the novel poses while maintaining the original style using an
image translator. Since images corresponding to the novel poses are not
available in training, the image translator is trained to be applicable only
when the input and target poses are similar, thus training does not require the
novel poses and their corresponding images. Next, we select a sequence of
target novel poses from the novel pose set, using Dijkstra's algorithm to
ensure that poses closer to each other are located adjacently in the sequence.
Finally, we repeatedly apply the image translator to each target pose in
sequence to produce a group of novel pose images representing a variety of
different limited body movements from the source pose. Experiments demonstrate
that, regardless of how the synthetic data is used for training or the data
size, leveraging the pose-diversified synthetic dataset in training generally
presents remarkably better accuracy than using the original synthetic dataset
on three aerial-view human detection benchmarks (VisDrone, Okutama-Action, and
ICG) in the few-shot regime.
