DeepLandscape: Adversarial Modeling of Landscape Video

By Elizaveta Logacheva, Roman Suvorov, Oleg Khomenko, Anton Mashikhin, Victor S. Lempitsky. 2020

26 citations recorded by the source corpus.

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 this https URL

Subfields: Computer Graphics and Computer-Aided Design, Computer Vision and Pattern Recognition

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In Computer Graphics and Computer-Aided Design

In Computer Vision and Pattern Recognition

Citations in the corpus

Stochastic Image-to-Video Synthesis using cINNs (2021, methodology): “DeepLandscape [47] (DL) does not learn dynamics from videos, but rather uses a manually constructed set of homographies.”