StyleCineGAN: Landscape Cinemagraph Generation using a Pre-trained
  StyleGAN
Jongwoo Choi Kwanggyoon Seo Amirsaman Ashtari Junyong Noh
We propose a method that can generate cinemagraphs automatically from a still
landscape image using a pre-trained StyleGAN. Inspired by the success of recent
unconditional video generation, we leverage a powerful pre-trained image
generator to synthesize high-quality cinemagraphs. Unlike previous approaches
that mainly utilize the latent space of a pre-trained StyleGAN, our approach
utilizes its deep feature space for both GAN inversion and cinemagraph
generation. Specifically, we propose multi-scale deep feature warping (MSDFW),
which warps the intermediate features of a pre-trained StyleGAN at different
resolutions. By using MSDFW, the generated cinemagraphs are of high resolution
and exhibit plausible looping animation. We demonstrate the superiority of our
method through user studies and quantitative comparisons with state-of-the-art
cinemagraph generation methods and a video generation method that uses a
pre-trained StyleGAN.