  Learning to represent and generate videos from unlabeled data is a very
challenging problem. To generate realistic videos, it is important not only to
ensure that the appearance of each frame is real, but also to ensure the
plausibility of a video motion and consistency of a video appearance in the
time direction. The process of video generation should be divided according to
these intrinsic difficulties. In this study, we focus on the motion and
appearance information as two important orthogonal components of a video, and
propose Flow-and-Texture-Generative Adversarial Networks (FTGAN) consisting of
FlowGAN and TextureGAN. In order to avoid a huge annotation cost, we have to
explore a way to learn from unlabeled data. Thus, we employ optical flow as
motion information to generate videos. FlowGAN generates optical flow, which
contains only the edge and motion of the videos to be begerated. On the other
hand, TextureGAN specializes in giving a texture to optical flow generated by
FlowGAN. This hierarchical approach brings more realistic videos with plausible
motion and appearance consistency. Our experiments show that our model
generates more plausible motion videos and also achieves significantly improved
performance for unsupervised action classification in comparison to previous
GAN works. In addition, because our model generates videos from two independent
information, our model can generate new combinations of motion and attribute
that are not seen in training data, such as a video in which a person is doing
sit-up in a baseball ground.
