  Its numerous applications make multi-human 3D pose estimation a remarkably
impactful area of research. Nevertheless, assuming a multiple-view system
composed of several regular RGB cameras, 3D multi-pose estimation presents
several challenges. First of all, each person must be uniquely identified in
the different views to separate the 2D information provided by the cameras.
Secondly, the 3D pose estimation process from the multi-view 2D information of
each person must be robust against noise and potential occlusions in the
scenario. In this work, we address these two challenges with the help of deep
learning. Specifically, we present a model based on Graph Neural Networks
capable of predicting the cross-view correspondence of the people in the
scenario along with a Multilayer Perceptron that takes the 2D points to yield
the 3D poses of each person. These two models are trained in a self-supervised
manner, thus avoiding the need for large datasets with 3D annotations.
