  We propose a bootstrapping framework to enhance human optical flow and pose.
We show that, for videos involving humans in scenes, we can improve both the
optical flow and the pose estimation quality of humans by considering the two
tasks at the same time. We enhance optical flow estimates by fine-tuning them
to fit the human pose estimates and vice versa. In more detail, we optimize the
pose and optical flow networks to, at inference time, agree with each other. We
show that this results in state-of-the-art results on the Human 3.6M and 3D
Poses in the Wild datasets, as well as a human-related subset of the Sintel
dataset, both in terms of pose estimation accuracy and the optical flow
accuracy at human joint locations. Code available at
https://github.com/ubc-vision/bootstrapping-human-optical-flow-and-pose
