  Video-to-video synthesis (vid2vid) aims at converting an input semantic
video, such as videos of human poses or segmentation masks, to an output
photorealistic video. While the state-of-the-art of vid2vid has advanced
significantly, existing approaches share two major limitations. First, they are
data-hungry. Numerous images of a target human subject or a scene are required
for training. Second, a learned model has limited generalization capability. A
pose-to-human vid2vid model can only synthesize poses of the single person in
the training set. It does not generalize to other humans that are not in the
training set. To address the limitations, we propose a few-shot vid2vid
framework, which learns to synthesize videos of previously unseen subjects or
scenes by leveraging few example images of the target at test time. Our model
achieves this few-shot generalization capability via a novel network weight
generation module utilizing an attention mechanism. We conduct extensive
experimental validations with comparisons to strong baselines using several
large-scale video datasets including human-dancing videos, talking-head videos,
and street-scene videos. The experimental results verify the effectiveness of
the proposed framework in addressing the two limitations of existing vid2vid
approaches.
