3D Human Pose Estimation in Video With Temporal Convolutions and Semi Supervised Training
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
2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)