  Cinemagraphs are short looping videos created by adding subtle motions to a
static image. This kind of media is popular and engaging. However, automatic
generation of cinemagraphs is an underexplored area and current solutions
require tedious low-level manual authoring by artists. In this paper, we
present an automatic method that allows generating human cinemagraphs from
single RGB images. We investigate the problem in the context of dressed humans
under the wind. At the core of our method is a novel cyclic neural network that
produces looping cinemagraphs for the target loop duration. To circumvent the
problem of collecting real data, we demonstrate that it is possible, by working
in the image normal space, to learn garment motion dynamics on synthetic data
and generalize to real data. We evaluate our method on both synthetic and real
data and demonstrate that it is possible to create compelling and plausible
cinemagraphs from single RGB images.
