## 1 Introduction  
Measurement and modeling of human body movement is a research field with many visual, medical, and monitoring applications. The data source initially used by scientists was video material. Many methods of pose tracking have been developed on the basis of image sequences, but a lack of information about 3D geometry has been a significant limitation. With the emergence of cheap RGBD sensors, there has been growing interest among scientists in the analysis of unidirectional 3D scans [1, 2]. A number of publications have focused on the analysis of unidirectional RGBD data, but the related methods still yield low-resolution output data and suffer from a number of problems, particularly with respect to position estimation from partial views, due to sensor noise and the problem of occlusion [2, 3]. The use of 4D scanners (3D scanners capable to capture geometry multiple times per second) is cost-intensive and, so, has remained reserved to a small group of researchers who have access to the necessary equipment. With the emergence of public scanning datasets, the topic of high-resolution reconstruction of motion and deformation of the human body has gained popularity. Reconstructing the movement and shape of a body on the basis of a sequence of 3D scans is a challenging task, due to the deformations of body shape deviating from rigid body dynamics and due to the amount and nature of the input data. The measurement data take the form of a series of point clouds or triangle meshes, where the vertices are not correlated with each other between individual moments in time (i.e., the number of vertices/points can change from frame to frame). In computer graphics applications,a homogeneous topology of geometry in consecutive sequence frames is a basic requirement, in view of rendering performance. Character animation also requires the definition of an animation skeleton for skinning the mesh.  
The aim of this work is to transfer changes in both the body shape and the pose of the animation skeleton on the basis of 4D scans with high spatial and temporal resolution.  
The main contributions of this work are as follows:  
* The development of a method to transfer human body surface deformations with geometrical accuracy better than current solutions (one of which is taken as a reference method);
* The introduction of shape maps as a tool to transfer local deformations;
* The incorporation of the Iterative Closest Point method (in the form of Point-to-Plane [4; 5], with rejection and optimization, using the Levenberg-Marquardt solver [6; 7]) with motion constraints for pose tracking in 4D scan sequences with high temporal and spatial resolution;
* Enabling the selection of a different mesh topology and resolution, as well as a skeleton, for each measurement sequence, without needing to retrain the model;
* Higher-resolution output meshes than state-of-the-art methods.  
The remainder of the article is organized as follows: in Section 2, we give an overview of research on 4D data analysis and related topics. In Section 3.1, we provide details about the scanning data used in this work; in Section 3.2, we present the general outline of our algorithm; and, in Sections 3.3 and 3.4, we describe the skeleton tracking and shape transfer methods, respectively, in detail. In Section 4, we present the results of an evaluation of the reconstruction quality of our method, compared to the reference method. The article concludes with a summary and description of future work, in Section 5.