  Generating videos from text has proven to be a significant challenge for
existing generative models. We tackle this problem by training a conditional
generative model to extract both static and dynamic information from text. This
is manifested in a hybrid framework, employing a Variational Autoencoder (VAE)
and a Generative Adversarial Network (GAN). The static features, called "gist,"
are used to sketch text-conditioned background color and object layout
structure. Dynamic features are considered by transforming input text into an
image filter. To obtain a large amount of data for training the deep-learning
model, we develop a method to automatically create a matched text-video corpus
from publicly available online videos. Experimental results show that the
proposed framework generates plausible and diverse videos, while accurately
reflecting the input text information. It significantly outperforms baseline
models that directly adapt text-to-image generation procedures to produce
videos. Performance is evaluated both visually and by adapting the inception
score used to evaluate image generation in GANs.
