Summary of Stylecinegan: Landscape Cinemagraph Generation Using a Pre-trained Stylegan, by Jongwoo Choi et al.
StyleCineGAN: Landscape Cinemagraph Generation using a Pre-trained StyleGAN
by Jongwoo Choi, Kwanggyoon Seo, Amirsaman Ashtari, Junyong Noh
First submitted to arxiv on: 21 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Graphics (cs.GR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed method generates high-quality cinemagraphs from still landscape images using a pre-trained StyleGAN. Building on recent successes in unconditional video generation, this approach leverages the generator’s deep feature space for both inversion and cinemagraph synthesis. A novel technique called multi-scale deep feature warping (MSDFW) is introduced, which warps features at different resolutions to produce realistic looping animations. The method’s effectiveness is demonstrated through user studies and quantitative comparisons with state-of-the-art approaches. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper uses a special kind of computer program to make still images move like movies. It takes an ordinary picture and turns it into a short video that looks like it’s always moving, but actually loops back to the beginning. This is called a cinemagraph. The researchers used a very powerful tool to do this, called StyleGAN. They showed how their method makes better videos than others in the same field. |
