  We build a new model of landscape videos that can be trained on a mixture of
static landscape images as well as landscape animations. Our architecture
extends StyleGAN model by augmenting it with parts that allow to model dynamic
changes in a scene. Once trained, our model can be used to generate realistic
time-lapse landscape videos with moving objects and time-of-the-day changes.
Furthermore, by fitting the learned models to a static landscape image, the
latter can be reenacted in a realistic way. We propose simple but necessary
modifications to StyleGAN inversion procedure, which lead to in-domain latent
codes and allow to manipulate real images. Quantitative comparisons and user
studies suggest that our model produces more compelling animations of given
photographs than previously proposed methods. The results of our approach
including comparisons with prior art can be seen in supplementary materials and
on the project page https://saic-mdal.github.io/deep-landscape
