Computer Vision – ECCV 2018

Abstract

We propose a light-weight yet highly robust method for real- time human performance capture based on a single depth camera and sparse inertial measurement units (IMUs). Our method combines non- rigid surface tracking and volumetric fusion to simultaneously recon- struct challenging motions, detailed geometries and the inner human body of a clothed subject. The proposed hybrid motion tracking algo- rithm and efficient per-frame sensor calibration technique enable non- rigid surface reconstruction for fast motions and challenging poses with severe occlusions. Significant fusion artifacts are reduced using a new confidence measurement for our adaptive TSDF-based fusion. The above contributions are mutually beneficial in our reconstruction system, which enable practical human performance capture that is real-time, robust, low-cost and easy to deploy. Experiments show that extremely challeng- ing performances and loop closure problems can be handled successfully. Keywords: