Improving fit of bicycle helmet liners using 3D anthropometric data

3D anthropometry has provided much-needed information about the size and shape of the head, which can be used to improve the fit of protective helmets. In this study, a new 3D head scan sizing method was implemented in a reverse engineering approach for bicycle helmet liner dimensioning. The inside liner of a commercially available helmet was modified to improve the fit for a selected size group of 30 participants. The fit of the standard and new liner were assessed and compared, using the Helmet Fit Index (HFI). The HFI scores showed a significant improvement of overall fit (Difference: 11.32 ± 7.82 (μ ± SD), p < 0.0005) and for each of five defined regions of the liner inside surface. The presented methodology for dimensioning helmet liners based on 3D anthropometry proved effective, resulting in improved fit for the end users.     

Taiwanese adult foot shape classification using 3D scanning data

This study classifies the foot shapes of Taiwanese using 3D foot scanning data from 2000 males and 1000 females. Nine foot dimensions relative to foot length and absolute measures in the common foot length categories were applied to compare the gender differences. Using foot breadth in % foot length (% FL), ball of foot length in % FL and arch height in % FL as feature parameters, three foot shape types for males and females can be classified. Significant gender differences were found in seven of the nine foot dimensions. Females had greater ball of foot length than males (0.2% FL). When comparing feet of the same foot length, males had greater breadth, girth and height dimensions than females, except for toe height. In addition, ethnic differences in foot shape were also observed. The findings can provide very useful information for building gender-specific shoe lasts and designing footwear insoles.     

个性化三维人体模型快速建模方法

采用模型重用的思想,通过输入21个人体测量学参数对标准人体模型进行编辑,实时获得相应体型的个性化三维人体模型．该方法使得编辑过程能够实时完成;生成的人体模型可以方便地驱动;精选的21个人体测量学参数覆盖了人体各处的细节,能够比较完整地描述人体的外形特征,满足对人体模型个性化的需求．     

Parametric 3D modeling of young women's lower bodies based on shape classification

Three-dimensional (3D) human body modeling is an important research direction in the field of clothing virtual design. On the basis of 3D human body scanning, this paper studied a method to build a 3D parametric lower body model according to body classification. The research includes three main parts. (1) Anthropometry and body shape classification. We randomly selected 333 young women ages 18–25 years old in Northeast China as the experimental sample. Then we divided the lower body shape into three categories using principal component analysis and K-means clustering. (2) Determination of feature cross sections and points, and reconstruction of feature curves. According to the average values of each body type, we obtained the mean reference body by Euclidean distance method. We determined feature cross sections and points, and extracted the 3D coordinates of the feature points of the mean reference body to reconstruct the feature curves. (3) The surface lofting and establishment of parametric 3D lower body model. According to the shape characteristics of the lower body, we constructed the guiding lines for the crotch and lower limbs, and established parametric lower body models for three body types.Relevance to industry3D human modeling is an important part of garment industry digitization. This research provides an effective way to construct a parametric 3D lower body model. The method offers a reference for the parametric virtual human modeling and virtual fitting of trousers.     

使用形状变化描述三维模型

针对三维模型检索中的形状特征提取问题,提出利用三维模型自身形状变化信息构造形状特征描述符的方法.首先选择一组等间距互相平行的平面切割三维模型,得到三维模型的切片集合;然后定义相邻切片的 差来描述切片间的形状变化,并通过所有相邻切片间的差值来反映三维模型自身的形状变化,以此作为三维模型的形状特征描述符.该方法与三维模型的旋转、平移无关,同时不依赖于模型的点云分布,并且精简模型三角面片对算法的影响较小.实验结果验证了该方法的有效性.     

草绘3维人体建模的模板形变方法

目的 采用草绘交互方式直接构造3维人体模型是当前人体建模研究的重要课题之一.提出一种草绘3维人体建模的模板形变方法.方法 针对输入的草图,首先,采用关节点定位方法获取草图中的人体关节点,根据人体结构学约束识别人体骨架结构,通过解析人体轮廓草图获取人体草图特征;其次,通过骨架模板和外观轮廓模板形变,将草图特征映射到3维人体模型,实现3维人体建模.结果 草图解析方法能有效提取草图特征,通过模板形变方法生成3维人体模型,并在模型上保持草图特征;能适应不同用户的绘制习惯,且生成的3维人体模型可用于人体动画设计.结论 提出一种草绘3维人体建模的模板形变方法,支持用户采用草绘方式进行3维人体模型设计,方法具有良好的用户适应性,对3维动画创作具有重要意义.     

Action-specific motion prior for efficient Bayesian 3D human body tracking

In this paper, we aim to reconstruct the 3D motion parameters of a human body model from the known 2D positions of a reduced set of joints in the image plane. Towards this end, an action-specific motion model is trained from a database of real motion-captured performances, and used within a particle filtering framework as a priori knowledge on human motion. First, our dynamic model guides the particles according to similar situations previously learnt. Then, the state space is constrained so only feasible human postures are accepted as valid solutions at each time step. As a result, we are able to track the 3D configuration of the full human body from several cycles of walking motion sequences using only the 2D positions of a very reduced set of joints from lateral or frontal viewpoints.     

一种新型人体三维建模系统的设计

设计了一款新型的人体三维建模识别系统。系统采用2片TI TMS320C64x+数字信号处理器组成算法核心单元,采用S3C6410嵌入式处理器实现时序及总线控制功能。该系统无需人体佩戴任何骨骼传感器网络,仅通过光学摄像头及图像处理算法即可完成人体三维运动建模,并可实现多人体运动目标的实时追踪功能。给出了该系统的实时多人三维运动建模结果,实测效果良好。     

PCA-based 3D shape reconstruction of human foot using multiple viewpoint cameras

This paper describes a multiple camera-based method to reconstruct the 3D shape of a human foot. From a foot database, an initial 3D model of the foot represented by a cloud of points is built. The shape parameters, which can characterize more than 92% of a foot, are defined by using the principal component analysis method. Then, using ＂active shape models＂, the initial 3D model is adapted to the real foot captured in multiple images by applying some constraints （edge points＇ distance and color variance）. We insist here on the experiment part where we demonstrate the efficiency of the proposed method on a plastic foot model, and also on real human feet with various shapes. We propose and compare different ways of texturing the foot which is needed for reconstruction. We present an experiment performed on the plastic foot model and on human feet and propose two different ways to improve the final 3D shapers accuracy according to the previous experiments＇ results. The first improvement proposed is the densification of the cloud of points used to represent the initial model and the foot database. The second improvement concerns the projected patterns used to texture the foot. We conclude by showing the obtained results for a human foot with the average computed shape error being only 1.06 mm.     

3D shape acquisition and integral compact representation using optical scanning and enhanced shape parameterization

An efficient computational methodology for shape acquisition, processing and representation is developed. It includes 3D computer vision by applying triangulation and stereo-photogrammetry for high-accuracy 3D shape acquisition. Resulting huge 3D point clouds are successively parameterized into mathematical surfaces to provide for compact data-set representation, yet capturing local details sufficiently. B-spline surfaces are employed as parametric entities in fitting to point clouds resulting from optical 3D scanning. Beyond the linear best-fitting algorithm with control points as fitting variables, an enhanced non-linear procedure is developed. The set of best fitting variables in minimizing the approximation error norm between the parametric surface and the 3D cloud includes the control points coordinates. However, they are augmented by the set of position parameter values which identify the respectively closest matching points on the surface for the points in the cloud. The developed algorithm is demonstrated to be efficient on demanding test cases which encompass sharp edges and slope discontinuities originating from physical damage of the 3D objects or shape complexity.     

Progressive 3D shape segmentation using online learning

In this article, we propose a progressive 3D shape segmentation method, which allows users to guide the segmentation with their interactions, and does segmentation gradually driven by their intents. More precisely, we establish an online framework for interactive 3D shape segmentation, without any boring collection preparation or training stages. That is, users can collect the 3D shapes while segment them, and the segmentation will become more and more precise as the accumulation of the shapes.Our framework uses Online Multi-Class LPBoost (OMCLP) to train/update a segmentation model progressively, which includes several Online Random forests (ORFs) as the weak learners. Then, it performs graph cuts optimization to segment the 3D shape by using the trained/updated segmentation model as the optimal data term. There exist three features of our framework. Firstly, the segmentation model can be trained gradually during the collection of the shapes. Secondly, the segmentation results can be refined progressively until users’ requirements are met. Thirdly, the segmentation model can be updated incrementally without retraining all shapes when users add new shapes. Experimental results demonstrate the effectiveness of our approach.     

Reconstruction of 3D human body pose from stereo image sequences based on top-down learning

This paper presents a novel method for reconstructing a 3D human body pose from stereo image sequences based on a top-down learning method. However, it is inefficient to build a statistical model using all training data. Therefore, the training data is hierarchically divided into several clusters to reduce the complexity of the learning problem. In the learning stage, the human body model database is hierarchically constructed by classifying the training data into several sub-clusters with silhouette images. The data of each cluster in the bottom level is represented by a linear combination of examples. In the reconstruction stage, the proposed method hierarchically searches a cluster for the best matching silhouette image using a silhouette history image (SHI). Then, the 3D human body pose is reconstructed from a depth image using a linear combination of examples method. By using depth information to reconstruct 3D human body pose, the similar poses in silhouette images are estimated as different 3D human body poses. The experimental results demonstrate that the proposed method is efficient and effective for reconstructing 3D human body poses.     

基于IsoRank的三维耳廓形状匹配

基于IsoRank算法实现了耳廓剖分图的匹配,进而实现了基于耳廓三维形状的身份鉴别.基于主成分分析提取待匹配三维耳廓上的关键点,构造耳廓关键点的三维网格图;基于IsoRank算法求2个关键点三维网格图结点之间的对应关系,实现耳廓关键点的图匹配.由于采用了IsoRank算法,耳廓关键点网格图得到了全局对齐,两耳廓之间的整体匹配得到最大化.实验结果表明,基于IsoRank算法的耳廓匹配方法具有较低的时间复杂度以及较高的匹配精度和匹配效率.     

Non-rigid 3D shape tracking from multiview video

We present a fast and efficient non-rigid shape tracking method for modeling dynamic 3D objects from multiview video. Starting from an initial mesh representation, the shape of a dynamic object is tracked over time, both in geometry and topology, based on multiview silhouette and 3D scene flow information. The mesh representation of each frame is obtained by deforming the mesh representation of the previous frame towards the optimal surface defined by the time-varying multiview silhouette information with the aid of 3D scene flow vectors. The whole time-varying shape is then represented as a mesh sequence which can efficiently be encoded in terms of restructuring and topological operations, and small-scale vertex displacements along with the initial model. The proposed method has the ability to deal with dynamic objects that may undergo non-rigid transformations and topological changes. The time-varying mesh representations of such non-rigid shapes, which are not necessarily of fixed connectivity, can successfully be tracked thanks to restructuring and topological operations employed in our deformation scheme. We demonstrate the performance of the proposed method both on real and synthetic sequences.     

基于C3D数据的人体模型逆向动力学仿真分析方法

基于AnyBody人体建模仿真分析软件,采用试验得到的动作捕捉格式数据C3D文件,研究人体逆向动力学仿真分析方法,给出C3D驱动AnyBody人体运动仿真分析时的操作步骤、报错分析及其解决方案。仿真结果表明:人体模型基本参数设置、C3D参数设置、关键点的拟合和足底压力板参数调节是实现人体模型动态分析的关键,其能有效提高人体模型逆向动力学仿真精度。