  In this paper, we propose a style-based conditional video generative model.
We introduce a novel temporal generator based on a set of learned sinusoidal
bases. Our method learns dynamic representations of various actions that are
independent of image content and can be transferred between different actors.
Beyond the significant enhancement of video quality compared to prevalent
methods, we demonstrate that the disentangled dynamic and content permit their
independent manipulation, as well as temporal GAN-inversion to retrieve and
transfer a video motion from one content or identity to another without further
preprocessing such as landmark points.
