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

3D human pose estimation in video with temporal convolutions and semi-supervised training