StyleCineGAN：使用预训练的StyleGAN生成景观电影图

StyleCineGAN: Landscape Cinemagraph Generation using a Pre-trained StyleGAN

March 21, 2024

作者: Jongwoo Choi, Kwanggyoon Seo, Amirsaman Ashtari, Junyong Noh

cs.AI

摘要

我们提出了一种方法，可以利用预训练的StyleGAN从静止的风景图像自动生成cinemagraphs。受到最近无条件视频生成成功的启发，我们利用强大的预训练图像生成器来合成高质量的cinemagraphs。与先前主要利用预训练StyleGAN的潜在空间的方法不同，我们的方法利用其深度特征空间进行GAN反演和cinemagraph生成。具体而言，我们提出了多尺度深度特征扭曲（MSDFW），它在不同分辨率下扭曲预训练StyleGAN的中间特征。通过使用MSDFW，生成的cinemagraphs具有高分辨率，并展现出可信的循环动画。我们通过用户研究和与最先进的cinemagraph生成方法以及使用预训练StyleGAN的视频生成方法的定量比较，展示了我们方法的优越性。

English

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.