In this paper, we aim to improve the state-of-the-art video generative
adversarial networks (GANs) with a view towards multi-functional applications.
Our improved video GAN model does not separate foreground from background nor
dynamic from static patterns, but learns to generate the entire video clip
conjointly. Our model can thus be trained to generate - and learn from - a
broad set of videos with no restriction. This is achieved by designing a robust
one-stream video generation architecture with an extension of the
state-of-the-art Wasserstein GAN framework that allows for better convergence.
The experimental results show that our improved video GAN model outperforms
state-of-theart video generative models on multiple challenging datasets.
Furthermore, we demonstrate the superiority of our model by successfully
extending it to three challenging problems: video colorization, video
inpainting, and future prediction. To the best of our knowledge, this is the
first work using GANs to colorize and inpaint video clips.

本文旨在通过设计稳健的单流视频生成架构，并扩展最先进的 Wasserstein GAN 框架，从而改进视频生成对抗网络（GANs）的性能，成功地将其应用于视频着色、视频修复和未来预测等领域，实验结果表明，我们的模型在多个具有挑战性的数据集上表现优异。