Learning Temporal 3D Human Pose Estimation with Pseudo-Labels

• URL: http://arxiv.org/abs/2110.07578v1
• Date: Thu, 14 Oct 2021 17:40:45 GMT
• Title: Learning Temporal 3D Human Pose Estimation with Pseudo-Labels
• Authors: Arij Bouazizi and Ulrich Kressel and Vasileios Belagiannis
• Abstract summary: We present a simple, yet effective, approach for self-supervised 3D human pose estimation. We rely on triangulating 2D body pose estimates of a multiple-view camera system. Our method achieves state-of-the-art performance in the Human3.6M and MPI-INF-3DHP benchmarks.
• Score: 3.0954251281114513
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present a simple, yet effective, approach for self-supervised 3D human pose estimation. Unlike the prior work, we explore the temporal information next to the multi-view self-supervision. During training, we rely on triangulating 2D body pose estimates of a multiple-view camera system. A temporal convolutional neural network is trained with the generated 3D ground-truth and the geometric multi-view consistency loss, imposing geometrical constraints on the predicted 3D body skeleton. During inference, our model receives a sequence of 2D body pose estimates from a single-view to predict the 3D body pose for each of them. An extensive evaluation shows that our method achieves state-of-the-art performance in the Human3.6M and MPI-INF-3DHP benchmarks. Our code and models are publicly available at \url{https://github.com/vru2020/TM_HPE/}.

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