Abstract
In this paper, we present SegmentedFusion, a system which has the capability of reconstructing non-rigid motion of a human model by using a single depth camera with skeleton information. Our approach estimates a dense volumetric 6D motion field that warps the integrated model into the live frame by segmenting body into different parts and building a canonical space for each part. The key feature of this work is that a deformed and connected canonical bounding-box for each part is created, and is used as a volume to integrate data. The dense volumetric warp field of one volume is represented efficiently by using two sets of rigid transformation parameters. Overall, SegmentedFusion is a system which is able to memory-efficiently scan non-rigidly deformed human surface as well as estimating dense motion field by using a consumer-grade depth camera. The experimental results demonstrate that our system is robust against fast inter-frame motion and topology changes. Since our method does not require prior assumption, the SegmentedFusion can be applied to real data which contains a wide range of human motion.