title: StyleCineGAN: Landscape Cinemagraph Generation using a Pre-trained
    StyleGAN
summary: 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.
paper_authors._id: 65fcebbc51eca95fc30769ca
paper_authors.name: Jongwoo Choi
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-03-22T10:29:31.090Z
paper_authors._id: 64abe3efeb79585dfb6fb32a
paper_authors.avatarUrl: /avatars/ee3dccf53f895c2c12a9ac95def4c927.svg
paper_authors.fullname: Jongwoo Choi
paper_authors.type: user
paper_authors.user: jeolpyeoni
paper_authors._id: 65fcebbc51eca95fc30769cb
paper_authors.name: Kwanggyoon Seo
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-03-22T10:30:05.792Z
paper_authors._id: 63628c4419cf373a5fc15422
paper_authors.avatarUrl: /avatars/06ce6da53caf6cf49f797ef8c6491fc5.svg
paper_authors.fullname: Kwanggyoon Seo
paper_authors.type: user
paper_authors.user: skg1023
paper_authors._id: 65fcebbc51eca95fc30769cc
paper_authors.name: Amirsaman Ashtari
paper_authors._id: 65fcebbc51eca95fc30769cd
paper_authors.name: Junyong Noh
paper_title: StyleCineGAN: Landscape Cinemagraph Generation using a Pre-trained
    StyleGAN
paper_summary: 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.
paper_ai_keywords: StyleGAN latent space deep feature space GAN inversion multi-scale deep feature warping (MSDFW) intermediate features high resolution plausible looping animation