Label-Conditioned Next-Frame Video Generation with Neural Flows

TL;DR

This paper introduces a neural flow-based model called Glow for label-conditioned next-frame video generation, offering more stability and better evaluation metrics than traditional GANs or VAEs.

Contribution

The work demonstrates how to adapt Glow, a neural flow model, for conditional video generation and evaluates its performance using cross entropy, addressing stability and evaluation issues.

Findings

Glow provides stable training compared to GANs.

Conditional Glow can generate videos based on textual labels.

Cross entropy effectively evaluates model performance.

Abstract

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.