Generalized Sum of Gaussians for Real-Time Human Pose Tracking from a Single Depth Sensor

Abstract

We propose a generalized Sum-of-Gaussians (G-SoG) model for statistical 3D shape modeling that is applied to human pose tracking from a single depth sensor. G-SoG generalizes the original SoG model by involving much fewer anisotropic Gaussians yet with better flexibility and adaptability. Both SoG and G-SoG are involved for pose tracking with different roles, where the former one is used to represent observed point cloud data through an efficient Octree partitioning, and the latter one is embedded with a quaternion-based articulated skeleton to create a standard human template model. We derive a differentiable similarity function between SoG and G-SoG that can be optimized analytically not only to learn a subject-specific articulated model but also to support sequential pose tracking where two additional terms (visibility and continuity) are also involved. Our algorithm is simple yet effective and can achieve real-time performance. The experimental results on a public depth dataset are promising and competitive when compared with state-of-the-art algorithms.