  In this work, we demonstrate that 3D poses in video can be effectively
estimated with a fully convolutional model based on dilated temporal
convolutions over 2D keypoints. We also introduce back-projection, a simple and
effective semi-supervised training method that leverages unlabeled video data.
We start with predicted 2D keypoints for unlabeled video, then estimate 3D
poses and finally back-project to the input 2D keypoints. In the supervised
setting, our fully-convolutional model outperforms the previous best result
from the literature by 6 mm mean per-joint position error on Human3.6M,
corresponding to an error reduction of 11%, and the model also shows
significant improvements on HumanEva-I. Moreover, experiments with
back-projection show that it comfortably outperforms previous state-of-the-art
results in semi-supervised settings where labeled data is scarce. Code and
models are available at https://github.com/facebookresearch/VideoPose3D
