  The performance of supervised deep learning algorithms depends significantly
on the scale, quality and diversity of the data used for their training.
Collecting and manually annotating large amount of data can be both
time-consuming and costly tasks to perform. In the case of tasks related to
visual human-centric perception, the collection and distribution of such data
may also face restrictions due to legislation regarding privacy. In addition,
the design and testing of complex systems, e.g., robots, which often employ
deep learning-based perception models, may face severe difficulties as even
state-of-the-art methods trained on real and large-scale datasets cannot always
perform adequately due to not having been adapted to the visual differences
between the virtual and the real world data. As an attempt to tackle and
mitigate the effect of these issues, we present a method that automatically
generates realistic synthetic data with annotations for a) person detection, b)
face recognition, and c) human pose estimation. The proposed method takes as
input real background images and populates them with human figures in various
poses. Instead of using hand-made 3D human models, we propose the use of models
generated through deep learning methods, further reducing the dataset creation
costs, while maintaining a high level of realism. In addition, we provide
open-source and easy to use tools that implement the proposed pipeline,
allowing for generating highly-realistic synthetic datasets for a variety of
tasks. A benchmarking and evaluation in the corresponding tasks shows that
synthetic data can be effectively used as a supplement to real data.
