Video Generation From Text


  • Yitong Li Duke University
  • Martin Min NEC Laboratories America
  • Dinghan Shen Duke University
  • David Carlson Duke University
  • Lawrence Carin Duke University



video generation, variational autoencoder, generative adversarial network


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 short-duration smooth 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.




How to Cite

Li, Y., Min, M., Shen, D., Carlson, D., & Carin, L. (2018). Video Generation From Text. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1).