Label-Conditioned Next-Frame Video Generation with Neural Flows

--Recent state-of-the-art video generation systems employ Generative Adversarial Networks (GANs) or V ariational Autoencoders (V AEs) to produce novel videos. However, V AE 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. I NTRODUCTION Text-to-video generation is the process by which a model conditions on text, and produces a video based on that text description. This is the exact opposite of video captioning, which aims to produce a caption that would describe a given video [6]. It may be argued that text-to-video is a harder task, as there are many more degrees of freedom in pixel space.