Robust Monocular 3D Human Motion with Lasso-Based Differential Kinematics

Abstract

This work introduces a method to robustly reconstruct 3D human motion from the motion of 2D skeletal landmarks. We propose to use a lasso (least absolute shrinkage and selection operator) optimization framework where the ℓ1-norm is computed over the vector of differential angular kinematics and the ℓ2-norm is computed over the differential 2D reprojection error. The ℓ1-norm term allows us to model sparse kinematic angular motion. The minimization of the reprojection error allows us to assume a bounded noise in both the kinematic model and the 2D landmark detection. This bound is controlled by a scale factor associated to the ℓ2-norm data term. A posteriori verification condition is provided to check whether or not the lasso formulation has allowed us to recover the ground-truth 3D human motion. Results on publicly available data demonstrates the effectiveness of the proposed approach on state-of-the-art methods. It shows that both sparsity and bounded noise assumptions encoded in lasso formulation are robust priors to safely recover 3D human motion.