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

翻译：我们建议一种新颖的稀有限制配方, 并从中得出一种3D人造型和形状估计的实时优化方法。 我们的优化方法SOPE(3D人造型和碎片估计的精度优化)比现有的优化方法要快, 数量级比现有优化方法快( 平均4 ms 趋同), 同时在数学上相当于其在轻度假设下密集而不受限制的配方。 我们通过利用我们制成的基点的广度和限制来有效计算高斯- 牛顿方向, 来实现这一目标。 我们的架构比其他最优化方法要优于其他最优化方法, 并实现与回归方法相比的竞争性精确性。 我们的代码和视频项目页面 : https:// humansite/glevelopy. orge: https:// homan-cloveal. orgo.