HybridFusion: Real-Time Performance Capture Using a Single Depth Sensor and Sparse IMUs

Zerong Zheng, Tao Yu, Hao Li, Kaiwen Guo, Qionghai Dai, Lu Fang, Yebin Liu

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 reconstruct challenging motions, detailed geometries and the inner human body of a clothed subject. The proposed hybrid motion tracking algorithm 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 challenging performances and loop closure problems can be handled successfully.