GANs are able to perform generation and manipulation tasks, trained on a
single video. However, these single video GANs require unreasonable amount of
time to train on a single video, rendering them almost impractical. In this
paper we question the necessity of a GAN for generation from a single video,
and introduce a non-parametric baseline for a variety of generation and
manipulation tasks. We revive classical space-time patches-nearest-neighbors
approaches and adapt them to a scalable unconditional generative model, without
any learning. This simple baseline surprisingly outperforms single-video GANs
in visual quality and realism (confirmed by quantitative and qualitative
evaluations), and is disproportionately faster (runtime reduced from several
days to seconds). Our approach is easily scaled to Full-HD videos. We also use
the same framework to demonstrate video analogies and spatio-temporal
retargeting. These observations show that classical approaches significantly
outperform heavy deep learning machinery for these tasks. This sets a new
baseline for single-video generation and manipulation tasks, and no less
important -- makes diverse generation from a single video practically possible
for the first time.

本文提出了非参数基线方法来解决单个视频生成及操作任务，相较于传统的基于 GAN 的方法能提供更高的性能和更快的运行速度，并且在视频类比和时空重新定位等方面表现突出。