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.

利用预训练的StyleGAN从静态风景图像中自动生成电影画面的方法，通过利用StyleGAN的深层特征空间进行GAN反演和电影画面生成，采用多尺度深层特征变形（MSDFW）技术，生成的电影画面具有高分辨率和可信的循环动画效果，并通过用户研究和与最先进的电影画面生成方法以及使用预训练的StyleGAN的视频生成方法的定量比较展示了我们方法的卓越性能。

StyleCineGAN：使用预训练的StyleGAN生成景观动态照片