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arXiv · 1811.11742

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

Dario Pavllo·Christoph Feichtenhofer·David Grangier·Michael Auli
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Abstract

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

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Dario Pavllo, Christoph Feichtenhofer, David Grangier, Michael Auli. 2019-03-29. 3D human pose estimation in video with temporal convolutions and semi-supervised training. https://arxiv.org/abs/1811.11742

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