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Virtual Reality Techniques Division, Institute of Micromechanics and Photonics, Faculty of Mechatronics, Warsaw University of Technology, ul. Św. Andrzeja Boboli 8, 02-525 Warsaw, Poland
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.
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.
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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 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 Section 3.3 and Section 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.
The appearance of low-cost RGBD cameras on the market (e.g., Kinect) has contributed to a growing interest in the subject of 4D data analysis by researchers worldwide [1, 2, 3, 8, 9, 10, 11, 12, 13, 14]. The data obtained by such sensors are heavily noisy, but their low price and additional information about the depth correlated with the RGB image has created new opportunities. The authors of Reference [3] used three such sensors, together with pressure sensors placed in shoes, for pose estimation and registration of the triangle mesh. Barros et al. [13] presented a method for pose estimation based on scans of a human body, using two opposite RGBD cameras and a pre-defined skeleton model. Once the skeleton base point is initialized with Principal Component Analysis (PCA), the individual scan parts are iteratively segmented and fitted based on Expectation Maximization (EM). In the skeleton model used, the geometric relationships between individual skeleton nodes are strictly defined. Some works also focused on hand movement tracking [9, 15, 16]. Tsoli and Argyros [15] proposed a joint optimization method through energy minimization to track motion of hand in contact with a cloth surface. They track both deformations of the object and pose of the hand interacting with it, based on data from a Kinect2 sensor. One must also note the research in robotical environment geometry discovery [17, 18, 19, 20]. One of the best-known groups of methods regarding this field is called Structure from Motion (SfM) [21, 22], where geometry of the environment is computed from a series 2D images taken from different viewpoints. To this group belongs the algorithm developed by Glannarou and Yang [17] which focuses on the reconstruction of a surgical environment envisioned by using an endoscopic camera. The authors incorporated Unscented Kalman Filter, along with data from Inertial Measurement Unit (IMU), to achieve deformable Structure from Motion. A work by Gotardo et al. [18] solves a rigid SfM problem by estimating the smooth time-trajectory of a camera moving around an object. Introduction of a parametrization in the Discrete Cosine Transform, along with the assumption of smooth camera trajectory, enabled the researchers to perform a non-rigid SfM in the presence of occlusions.
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To date, few works have focused on high-resolution data, due to high cost of obtaining such a system. One of the first collections of this type—which is available for a fee—is the CAESAR dataset (Civilian American and European Surface Anthropometry Resource Project) [23], containing about 4400 scans of different people. The pioneering work in the analysis of high-resolution 3D scans includes the SCAPE method (Shape Completion and Animation of People) [24], which is based on the model of body shape deformation as a function of the basic body shape of a given person and their pose at a specific moment. The authors presented the use of their method for the completion of unidirectional scans, as well as for the generation of mesh deformations in a sequence, based on a static scan of a person and a sequence of marker motions obtained by using the Motion Capture system. Another dataset of scans acquired with the use of a 3DMD scanner has been made publicly available, under the name Dynamic FAUST (DFAUST) [25], which has provided important motivation for research development in the field of high-resolution 4D scan analysis techniques [23, 25, 26, 27, 28, 29, 30, 31, 32, 33]. This dataset consists of thousands of 3D scans of various people in different poses. The authors of the dataset developed it, using the Skinned Multi-Person Linear (SMPL) model [34], which is a trained model of various body shapes and their deformations with pose changes. Next, the authors used this model for pose and shape estimation from 2D images through the incorporation of a Convolutional Neural Network (CNN) [27, 28]. The researchers also managed to develop an analogous model for infants (SMIL, Skinned Multi-Infant Linear model) using information from RGBD sequences [35, 36]. Dyna [37] is a model which describes soft-tissue deformations, depending on the body shape and motion dynamics.
To date, few works have focused on high-resolution data, due to high cost of obtaining such a system. One of the first collections of this type—which is available for a fee—is the CAESAR dataset (Civilian American and European Surface Anthropometry Resource Project) [23], containing about 4400 scans of different people. The pioneering work in the analysis of high-resolution 3D scans includes the SCAPE method (Shape Completion and Animation of People) [24], which is based on the model of body shape deformation as a function of the basic body shape of a given person and their pose at a specific moment. The authors presented the use of their method for the completion of unidirectional scans, as well as for the generation of mesh deformations in a sequence, based on a static scan of a person and a sequence of marker motions obtained by using the Motion Capture system. Another dataset of scans acquired with the use of a 3DMD scanner has been made publicly available, under the name Dynamic FAUST (DFAUST) [25], which has provided important motivation for research development in the field of high-resolution 4D scan analysis techniques [23, 25, 26, 27, 28, 29, 30, 31, 32, 33]. This dataset consists of thousands of 3D scans of various people in different poses. The authors of the dataset developed it, using the Skinned Multi-Person Linear (SMPL) model [34], which is a trained model of various body shapes and their deformations with pose changes. Next, the authors used this model for pose and shape estimation from 2D images through the incorporation of a Convolutional Neural Network (CNN) [27, 28]. The researchers also managed to develop an analogous model for infants (SMIL, Skinned Multi-Infant Linear model) using information from RGBD sequences [35, 36]. Dyna [37] is a model which describes soft-tissue deformations, depending on the body shape and motion dynamics.
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