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

CVPR 2019 Dario PavlloChristoph FeichtenhoferDavid GrangierMichael Auli

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... (read more)

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Evaluation results from the paper


Task Dataset Model Metric name Metric value Global rank Compare
3D Human Pose Estimation Human3.6M Temporal convolution + semi-supervision Average MPJPE (mm) 46.8 # 7