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.

本文提出了一种使用基于 patch 的变分自编码器（VAE）生成多样化视频的新方法，并将其与更细节的 patch-GAN 相结合，以在保证多样性的同时生成高质量视频。实验结果表明，该方法不仅可以生成多样化的图像，还可以处理更具挑战性的视频领域。