  We present a self-supervised learning algorithm for 3D human pose estimation
of a single person based on a multiple-view camera system and 2D body pose
estimates for each view. To train our model, represented by a deep neural
network, we propose a four-loss function learning algorithm, which does not
require any 2D or 3D body pose ground-truth. The proposed loss functions make
use of the multiple-view geometry to reconstruct 3D body pose estimates and
impose body pose constraints across the camera views. Our approach utilizes all
available camera views during training, while the inference is single-view. In
our evaluations, we show promising performance on Human3.6M and HumanEva
benchmarks, while we also present a generalization study on MPI-INF-3DHP
dataset, as well as several ablation results. Overall, we outperform all
self-supervised learning methods and reach comparable results to supervised and
weakly-supervised learning approaches. Our code and models are publicly
available

Computer Vision and Pattern Recognition