  In this work, we introduce a two-step framework for generative modeling of
temporal data. Specifically, the generative adversarial networks (GANs) setting
is employed to generate synthetic scenes of moving objects. To do so, we
propose a two-step training scheme within which: a generator of static frames
is trained first. Afterwards, a recurrent model is trained with the goal of
providing a sequence of inputs to the previously trained frames generator, thus
yielding scenes which look natural. The adversarial setting is employed in both
training steps. However, with the aim of avoiding known training instabilities
in GANs, a multiple discriminator approach is used to train both models.
Results in the studied video dataset indicate that, by employing such an
approach, the recurrent part is able to learn how to coherently navigate the
image manifold induced by the frames generator, thus yielding more
natural-looking scenes.
