  We address the task of unsupervised retargeting of human actions from one
video to another. We consider the challenging setting where only a few frames
of the target is available. The core of our approach is a conditional
generative model that can transcode input skeletal poses (automatically
extracted with an off-the-shelf pose estimator) to output target frames.
However, it is challenging to build a universal transcoder because humans can
appear wildly different due to clothing and background scene geometry. Instead,
we learn to adapt - or personalize - a universal generator to the particular
human and background in the target. To do so, we make use of meta-learning to
discover effective strategies for on-the-fly personalization. One significant
benefit of meta-learning is that the personalized transcoder naturally enforces
temporal coherence across its generated frames; all frames contain consistent
clothing and background geometry of the target. We experiment on in-the-wild
internet videos and images and show our approach improves over widely-used
baselines for the task.
