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

## TL;DR

This paper presents a two-step GAN-based framework for unsupervised video generation, where static frame generation is followed by recurrent modeling to produce coherent, natural-looking video scenes.

## Contribution

It introduces a novel two-step training scheme with multiple discriminators to improve the stability and coherence of generated videos.

## Key findings

- Recurrent model learns to navigate the image manifold for coherent scene generation.
- Multiple discriminator approach stabilizes training of both static and sequential models.
- Generated videos exhibit more natural and consistent motion compared to baseline methods.

## Abstract

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.