Publication Date: 2/2/2018
Event: The Thirty-Second AAAI Conference on Artificial Intelligence
Reference: pp. 1-8, 2018
Authors: Yitong Li, Duke University; Martin Renqiang Min, NEC Laboratories America, Inc.; Dinghan Shen, Duke University
Abstract: 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.
Publication Link: https://dl.acm.org/doi/10.5555/3504035.3504900