StyleCineGAN: Landscape Cinemagraph Generation using a Pre-trained
  StyleGAN
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.
65fcebbc51eca95fc30769ca
Jongwoo Choi
admin_assigned
2024-03-22T10:29:31.090Z
64abe3efeb79585dfb6fb32a
/avatars/ee3dccf53f895c2c12a9ac95def4c927.svg
Jongwoo Choi
user
jeolpyeoni
65fcebbc51eca95fc30769cb
Kwanggyoon Seo
admin_assigned
2024-03-22T10:30:05.792Z
63628c4419cf373a5fc15422
/avatars/06ce6da53caf6cf49f797ef8c6491fc5.svg
Kwanggyoon Seo
user
skg1023
65fcebbc51eca95fc30769cc
Amirsaman Ashtari
65fcebbc51eca95fc30769cd
Junyong Noh
StyleCineGAN: Landscape Cinemagraph Generation using a Pre-trained
  StyleGAN
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.
A method for automatic cinemagraph generation from still images using a pre-trained StyleGAN, employing multi-scale deep feature warping to achieve high-resolution, plausible looping animations.
StyleGAN latent space deep feature space GAN inversion multi-scale deep feature warping MSDFW cinemagraph generation looping animation user studies quantitative comparisons