Multi-person 3D pose estimation from unlabelled data

Abstract

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.

References in corpus (5)

• Joint Training of a Convolutional Network and a Graphical Model for Human Pose Estimation
• OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields
• XNect: Real-time Multi-Person 3D Motion Capture with a Single RGB Camera
• Learning Human Pose Estimation Features with Convolutional Networks
• Self-supervision on Unlabelled OR Data for Multi-person 2D/3D Human Pose Estimation