论文标题
分层补丁vae-gan：从单个样本中生成多种视频
Hierarchical Patch VAE-GAN: Generating Diverse Videos from a Single Sample
论文作者
论文摘要
我们考虑从单个视频样本中生成多样化和新颖的视频的任务。最近，提出了新的基于分层的方法来生成各种图像，仅在训练时只有一个样本。这些方法转向视频，无法生成各种样本，并且经常崩溃为类似于培训视频的样本。我们介绍了一种新型的基于贴片的变性自动编码器（VAE），该变性器可以使生成更大。使用此工具，构建了一种新的分层视频生成方案：在粗糙的尺度上，我们的斑块被采用，确保样品具有很高的多样性。随后，在更细的尺度上，一个补丁程序可提供精美的细节，从而产生高质量的视频。我们的实验表明，所提出的方法在图像域和更具挑战性的视频域中产生了不同的样本。
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.
