  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/}.
