  Recent state-of-the-art video generation systems employ Generative
Adversarial Networks (GANs) or Variational Autoencoders (VAEs) to produce novel
videos. However, VAE models typically produce blurry outputs when faced with
sub-optimal conditioning of the input, and GANs are known to be unstable for
large output sizes. In addition, the output videos of these models are
difficult to evaluate, partly because the GAN loss function is not an accurate
measure of convergence. In this work, we propose using a state-of-the-art
neural flow generator called Glow to generate videos conditioned on a textual
label, one frame at a time. Neural flow models are more stable than standard
GANs, as they only optimize a single cross entropy loss function, which is
monotonic and avoids the circular convergence issues of the GAN minimax
objective. In addition, we also show how to condition Glow on external context,
while still preserving the invertible nature of each "flow" layer. Finally, we
evaluate the proposed Glow model by calculating cross entropy on a held-out
validation set of videos, in order to compare multiple versions of the proposed
model via an ablation study. We show generated videos and discuss future
improvements.
