  Deep neural networks have become prevalent in human analysis, boosting the
performance of applications, such as biometric recognition, action recognition,
as well as person re-identification. However, the performance of such networks
scales with the available training data. In human analysis, the demand for
large-scale datasets poses a severe challenge, as data collection is tedious,
time-expensive, costly and must comply with data protection laws. Current
research investigates the generation of \textit{synthetic data} as an efficient
and privacy-ensuring alternative to collecting real data in the field. This
survey introduces the basic definitions and methodologies, essential when
generating and employing synthetic data for human analysis. We conduct a survey
that summarises current state-of-the-art methods and the main benefits of using
synthetic data. We also provide an overview of publicly available synthetic
datasets and generation models. Finally, we discuss limitations, as well as
open research problems in this field. This survey is intended for researchers
and practitioners in the field of human analysis.
