论文标题
Deeplandscape：景观视频的对抗建模
DeepLandscape: Adversarial Modeling of Landscape Video
论文作者
论文摘要
我们构建了一个新的景观视频模型，可以在静态景观图像和景观动画的混合物中进行培训。我们的体系结构通过使用允许在场景中建模动态更改的零件来扩展式模型。经过培训后，我们的模型可用于生成具有移动对象和当天更改的逼真的延时景观视频。此外，通过将学习模型拟合到静态景观图像中，可以以现实的方式重新制定后者。我们建议对StyleGAN反转过程进行简单但必要的修改，这导致了内域潜在的代码并允许操纵真实的图像。定量比较和用户研究表明，与以前提出的方法相比，我们的模型会产生更引人注目的动画。我们的方法的结果包括在补充材料和项目页面上可以看到https://saic-mdal.github.io/deep-landscape中的比较。
We build a new model of landscape videos that can be trained on a mixture of static landscape images as well as landscape animations. Our architecture extends StyleGAN model by augmenting it with parts that allow to model dynamic changes in a scene. Once trained, our model can be used to generate realistic time-lapse landscape videos with moving objects and time-of-the-day changes. Furthermore, by fitting the learned models to a static landscape image, the latter can be reenacted in a realistic way. We propose simple but necessary modifications to StyleGAN inversion procedure, which lead to in-domain latent codes and allow to manipulate real images. Quantitative comparisons and user studies suggest that our model produces more compelling animations of given photographs than previously proposed methods. The results of our approach including comparisons with prior art can be seen in supplementary materials and on the project page https://saic-mdal.github.io/deep-landscape
