Label-Conditioned Next-Frame Video Generation with Neural Flows
This work addresses video generation for applications requiring stable and high-quality outputs, but it appears incremental as it adapts an existing neural flow method to a new conditioning task.
The paper tackles the problem of generating videos conditioned on textual labels by proposing a neural flow generator (Glow) to produce videos one frame at a time, aiming to address issues like blurry outputs and instability in existing GAN and VAE models, with evaluation based on cross entropy on a validation set.
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.