MetaPix: Few-Shot Video Retargeting
This addresses the challenge of video retargeting for applications like content creation, but it is incremental as it builds on existing generative and meta-learning methods.
The paper tackles the problem of unsupervised retargeting of human actions from one video to another with only a few target frames, using a meta-learning approach to personalize a generative model, resulting in improved performance over baselines on in-the-wild internet videos and images.
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.