  Robotic applications involving people often require advanced perception
systems to better understand complex real-world scenarios. To address this
challenge, photo-realistic and physics simulators are gaining popularity as a
means of generating accurate data labeling and designing scenarios for
evaluating generalization capabilities, e.g., lighting changes, camera
movements or different weather conditions. We develop a photo-realistic
framework built on Unreal Engine and AirSim to generate easily scenarios with
pedestrians and mobile robots. The framework is capable to generate random and
customized trajectories for each person and provides up to 50 ready-to-use
people models along with an API for their metadata retrieval. We demonstrate
the usefulness of the proposed framework with a use case of multi-target
tracking, a popular problem in real pedestrian scenarios. The notable feature
variability in the obtained perception data is presented and evaluated.
