Structured Aleatoric Uncertainty in Human Pose Estimation

Abstract

Human pose estimation from monocular images exhibits an inherent uncertainty through self-occlusions and inter-person occlusions, aside from typical sources of uncertainty. Recently, there has been an increased focus in modelling uncertainty in supervised machine learning tasks. In line with this trend, we propose a novel formulation to capture aleatoric uncertainty in human pose using a multivariate Gaussian distribution over all the joints of human body and show that this improves generalization in 2D hu- man pose estimation by implicitly suppressing the gradients from uncertain joints. Further, we develop a novel method to triangulate 3D human pose from predicted 2D poses, under the predicted uncertainty, that out-performs the baselines by over 10.8% and provide a multi-view inference benchmark for 3D human pose estimation on Human 3.6M dataset.