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We propose a novel sparse constrained formulation and from it derive areal-time optimization method for 3D human pose and shape estimation. Ouroptimization method, SCOPE (Sparse Constrained Optimization for 3D human Poseand shapE estimation), is orders of magnitude faster (avg. 4 ms convergence)than existing optimization methods, while being mathematically equivalent totheir dense unconstrained formulation under mild assumptions. We achieve thisby exploiting the underlying sparsity and constraints of our formulation toefficiently compute the Gauss-Newton direction. We show that this computationscales linearly with the number of joints and measurements of a complex 3Dhuman model, in contrast to prior work where it scales cubically due to theirdense unconstrained formulation. Based on our optimization method, we present areal-time motion capture framework that estimates 3D human poses and shapesfrom a single image at over 30 FPS. In benchmarks against state-of-the-artmethods on multiple public datasets, our framework outperforms otheroptimization methods and achieves competitive accuracy against regressionmethods. Project page with code and videos:https://sites.google.com/view/scope-human/.

Revitalizing Optimization for 3D Human Pose and Shape Estimation: A Sparse Constrained Formulation