  Simulation is a powerful tool to easily generate annotated data, and a highly
desirable feature, especially in those domains where learning models need large
training datasets. Machine learning and deep learning solutions, have proven to
be extremely data-hungry and sometimes, the available real-world data are not
sufficient to effectively model the given task. Despite the initial skepticism
of a portion of the scientific community, the potential of simulation has been
largely confirmed in many application areas, and the recent developments in
terms of rendering and virtualization engines, have shown a good ability also
in representing complex scenes. This includes environmental factors, such as
weather conditions and surface reflectance, as well as human-related events,
like human actions and behaviors. We present a human crowd simulator, called
UniCrowd, and its associated validation pipeline. We show how the simulator can
generate annotated data, suitable for computer vision tasks, in particular for
detection and segmentation, as well as the related applications, as crowd
counting, human pose estimation, trajectory analysis and prediction, and
anomaly detection.
