  The extension of image generation to video generation turns out to be a very
difficult task, since the temporal dimension of videos introduces an extra
challenge during the generation process. Besides, due to the limitation of
memory and training stability, the generation becomes increasingly challenging
with the increase of the resolution/duration of videos. In this work, we
exploit the idea of progressive growing of Generative Adversarial Networks
(GANs) for higher resolution video generation. In particular, we begin to
produce video samples of low-resolution and short-duration, and then
progressively increase both resolution and duration alone (or jointly) by
adding new spatiotemporal convolutional layers to the current networks.
Starting from the learning on a very raw-level spatial appearance and temporal
movement of the video distribution, the proposed progressive method learns
spatiotemporal information incrementally to generate higher resolution videos.
Furthermore, we introduce a sliced version of Wasserstein GAN (SWGAN) loss to
improve the distribution learning on the video data of high-dimension and
mixed-spatiotemporal distribution. SWGAN loss replaces the distance between
joint distributions by that of one-dimensional marginal distributions, making
the loss easier to compute. We evaluate the proposed model on our collected
face video dataset of 10,900 videos to generate photorealistic face videos of
256x256x32 resolution. In addition, our model also reaches a record inception
score of 14.57 in unsupervised action recognition dataset UCF-101.
