In this article, we present a method of analysis for 3D scanning sequences of human bodies in motion that allows us to obtain a computer animation of a virtual character containing both skeleton motion and high-detail deformations of the body surface geometry, resulting from muscle activity, the dynamics of the motion, and tissue inertia. The developed algorithm operates on a sequence of 3D scans with high spatial and temporal resolution. The presented method can be applied to scans in the form of both triangle meshes and 3D point clouds. One of the contributions of this work is the use of the Iterative Closest Point algorithm with motion constraints for pose tracking, which has been problematic so far. We also introduce shape maps as a tool to represent local body segment deformations. An important feature of our method is the possibility to change the topology and resolution of the output mesh and the topology of the animation skeleton in individual sequences, without requiring time-consuming retraining of the model. Compared to the state-of-the-art Skinned Multi-Person Linear (SMPL) method, the proposed algorithm yields almost twofold better accuracy in shape mapping.

In this paper we introduce a cavity reconstructing algorithm for 3D surface scans (CRASS) developed for filling cavities in point clouds representing human body surfaces. The presented method uses Bezier patches to reconstruct missing data. The source of input data for the algorithm was an 8-directional structured light scanning system for the human body. Typical 3D scan representing human body consists of about 1 million points with average sampling density of 1 mm. The paper describes the complete scan processing pipeline: data pre-processing, boundary selection, cavity extraction and reconstruction, and a post-processing step to smooth and resample resulting geometry. The developed algorithm was tested on simulated and scanned 3D input data. Quality assessment was made based on simulated cavities, reconstructed using presented method and compared to original 3D geometry. Additionally, comparison to the state-of-the-art screened Poisson method is presented. Values’ ranges of parameters influencing result of described method were estimated for sample scans and comprehensively discussed. The results of the quantitative assessment of the reconstruction were lower than 0,5 of average sampling density.