(WACV 2021) SinGAN-GIF: Learning a Generative model from a single GIF

WACV 2021

(WACV 2021) SinGAN-GIF: Learning a Generative model from a single GIF

Jan 18, 2021
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Abstract: We propose SinGAN-GIF, an extension of the image based SinGAN to GIFs or short video snippets. Our method learns the distribution of both the image patches in the GIF as well as their motion patterns. We do so by using a pyramid of 3D and 2D convolutional networks to model temporal information while reducing model parameters and training time, along with an image and a video discriminator. SinGAN-GIF can generate similar looking video samples for natural scenes at different spatial resolutions or temporal frame rates, and can be extended to other video applications like video editing, super resolution, and motion transfer. Authors: Rajat Arora, Yong Jae Lee (University of California, Davis)

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