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

Learning to navigate image manifolds induced by generative adversarial networks for unsupervised video generation