  Tremendous variations coupled with large degrees of freedom in UAV-based
imaging conditions lead to a significant lack of data in adequately learning
UAV-based perception models. Using various synthetic renderers in conjunction
with perception models is prevalent to create synthetic data to augment the
learning in the ground-based imaging domain. However, severe challenges in the
austere UAV-based domain require distinctive solutions to image synthesis for
data augmentation. In this work, we leverage recent advancements in neural
rendering to improve static and dynamic novelview UAV-based image synthesis,
especially from high altitudes, capturing salient scene attributes. Finally, we
demonstrate a considerable performance boost is achieved when a state-ofthe-art
detection model is optimized primarily on hybrid sets of real and synthetic
data instead of the real or synthetic data separately.
