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Hierarchical Patch VAE-GAN: Generating Diverse Videos from a Single Sample
Shir Gur · Sagie Benaim · Lior Wolf

Tue Dec 08 09:00 PM -- 11:00 PM (PST) @ Poster Session 2 #624

We consider the task of generating diverse and novel videos from a single video sample. Recently, new hierarchical patch-GAN based approaches were proposed for generating diverse images, given only a single sample at training time. Moving to videos, these approaches fail to generate diverse samples, and often collapse into generating samples similar to the training video. We introduce a novel patch-based variational autoencoder (VAE) which allows for a much greater diversity in generation. Using this tool, a new hierarchical video generation scheme is constructed: at coarse scales, our patch-VAE is employed, ensuring samples are of high diversity. Subsequently, at finer scales, a patch-GAN renders the fine details, resulting in high quality videos. Our experiments show that the proposed method produces diverse samples in both the image domain, and the more challenging video domain. Our code and supplementary material (SM) with additional samples are available at https://shirgur.github.io/hp-vae-gan

Author Information

Shir Gur (Tel Aviv University)
Sagie Benaim (Tel Aviv University)
Lior Wolf (Facebook AI Research)

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