  Image and video synthesis are closely related areas aiming at generating
content from noise. While rapid progress has been demonstrated in improving
image-based models to handle large resolutions, high-quality renderings, and
wide variations in image content, achieving comparable video generation results
remains problematic. We present a framework that leverages contemporary image
generators to render high-resolution videos. We frame the video synthesis
problem as discovering a trajectory in the latent space of a pre-trained and
fixed image generator. Not only does such a framework render high-resolution
videos, but it also is an order of magnitude more computationally efficient. We
introduce a motion generator that discovers the desired trajectory, in which
content and motion are disentangled. With such a representation, our framework
allows for a broad range of applications, including content and motion
manipulation. Furthermore, we introduce a new task, which we call cross-domain
video synthesis, in which the image and motion generators are trained on
disjoint datasets belonging to different domains. This allows for generating
moving objects for which the desired video data is not available. Extensive
experiments on various datasets demonstrate the advantages of our methods over
existing video generation techniques. Code will be released at
https://github.com/snap-research/MoCoGAN-HD.
