StyleCineGAN: Landscape Cinemagraph Generation using a Pre-trained StyleGAN
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866914722863906816 |
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| author | Choi, Jongwoo Seo, Kwanggyoon Ashtari, Amirsaman Noh, Junyong |
| author_facet | Choi, Jongwoo Seo, Kwanggyoon Ashtari, Amirsaman Noh, Junyong |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_14186 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | StyleCineGAN: Landscape Cinemagraph Generation using a Pre-trained StyleGAN Choi, Jongwoo Seo, Kwanggyoon Ashtari, Amirsaman Noh, Junyong Computer Vision and Pattern Recognition Artificial Intelligence Graphics 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. |
| title | StyleCineGAN: Landscape Cinemagraph Generation using a Pre-trained StyleGAN |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Graphics |
| url | https://arxiv.org/abs/2403.14186 |