  Modern approaches for multi-person pose estimation in video require large
amounts of dense annotations. However, labeling every frame in a video is
costly and labor intensive. To reduce the need for dense annotations, we
propose a PoseWarper network that leverages training videos with sparse
annotations (every k frames) to learn to perform dense temporal pose
propagation and estimation. Given a pair of video frames---a labeled Frame A
and an unlabeled Frame B---we train our model to predict human pose in Frame A
using the features from Frame B by means of deformable convolutions to
implicitly learn the pose warping between A and B. We demonstrate that we can
leverage our trained PoseWarper for several applications. First, at inference
time we can reverse the application direction of our network in order to
propagate pose information from manually annotated frames to unlabeled frames.
This makes it possible to generate pose annotations for the entire video given
only a few manually-labeled frames. Compared to modern label propagation
methods based on optical flow, our warping mechanism is much more compact (6M
vs 39M parameters), and also more accurate (88.7% mAP vs 83.8% mAP). We also
show that we can improve the accuracy of a pose estimator by training it on an
augmented dataset obtained by adding our propagated poses to the original
manual labels. Lastly, we can use our PoseWarper to aggregate temporal pose
information from neighboring frames during inference. This allows our system to
achieve state-of-the-art pose detection results on the PoseTrack2017 and
PoseTrack2018 datasets. Code has been made available at:
https://github.com/facebookresearch/PoseWarper.
