  With the increasing interest in the content creation field in multiple
sectors such as media, education, and entertainment, there is an increasing
trend in the papers that uses AI algorithms to generate content such as images,
videos, audio, and text. Generative Adversarial Networks (GANs) in one of the
promising models that synthesizes data samples that are similar to real data
samples. While the variations of GANs models, in general, have been covered to
some extent in several survey papers, to the best of our knowledge, this is
among the first survey papers that reviews the state-of-the-art video GANs
models. This paper first categorized GANs review papers into general GANs
review papers, image GANs review papers, and special field GANs review papers
such as anomaly detection, medical imaging, or cybersecurity. The paper then
summarizes the main improvements in GANs frameworks that are not initially
developed for the video domain but have been adopted in multiple video GANs
variations. Then, a comprehensive review of video GANs models is provided under
two main divisions according to the presence or non-presence of a condition.
The conditional models then further grouped according to the type of condition
into audio, text, video, and image. The paper is concluded by highlighting the
main challenges and limitations of the current video GANs models. A
comprehensive list of datasets, applied loss functions, and evaluation metrics
is provided in the supplementary material.
