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.

本文研究多人姿态估计问题，提出了一种基于多视图系统和深度学习的方法，通过使用图神经网络模型预测场景中不同人之间的视角对应关系，并使用多层感知器模型将2D点转换为每个人的3D姿态。该模型采用自监督学习方式进行训练，从而避免了对大规模3D数据集的依赖。

多人无标记数据3D姿势估计