  Generative Adversarial Networks have recently shown promise for video
generation, building off of the success of image generation while also
addressing a new challenge: time. Although time was analyzed in some early
work, the literature has not adequately grown with temporal modeling
developments. We study the effects of Neural Differential Equations to model
the temporal dynamics of video generation. The paradigm of Neural Differential
Equations presents many theoretical strengths including the first continuous
representation of time within video generation. In order to address the effects
of Neural Differential Equations, we investigate how changes in temporal models
affect generated video quality. Our results give support to the usage of Neural
Differential Equations as a simple replacement for older temporal generators.
While keeping run times similar and decreasing parameter count, we produce a
new state-of-the-art model in 64$\times$64 pixel unconditional video
generation, with an Inception Score of 15.20.
