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.
This paper proposes a novel and interesting task that learn to retarget human actions with few-shot samples. The overall pipeline is built by applying  meta-learning strategy on pre-trained retargeting module. It follows a conditional generator and discriminator structure that leverages few-shot frames to retarget the action of source video. The approach is technically sound.
The evaluations are compared with two baseline methods,  Pix2PixHD and Posewarp. Their evaluations are satisfactory and convincing, the results demonstrate some improvements over baseline models regarding both the selected metrics and the visualizing performance.
Though the proposed problem is novel and somewhat interesting, there are also several weaknesses of this work:
- The novelty of methodology is somewhat limited. It is more about merging several state-of-the-art modules in different tasks to tackle the few-shot retargeting problem. Though efforts may be needed to make the pipeline work, the overall contribution is not significant. 
- The improvement obtained with proposed MetaPix module is not significant in few-shot setting according to Table 1. Additionally, could the authors provide some visualizing results for different K number, which would be interesting for analysis.