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。     

Kinect动作捕捉技术在三维动画教学中的应用与实践

针对传统三维角色动画制作成本高、时间长的问题,文章介绍了一种应用Kinect动作捕捉技术实现高效制作三维角色动画的方法。该方法借助Kinect体感摄像机捕捉真人的动作生成骨骼关节的关键帧数据并输出bvh动作路径文件,然后把bvh动作路径数据导入C4D软件中,就可以驱动角色模型完成角色动画的制作。把这种方法运用到三维角色动画教学实践中,有利于提高学生学习的兴趣和效率。     

基于动捕数据驱动的三维民族舞蹈展示方法

三维模型动画在数字化设计和应用中具有重要意义,受到越来越多研究者关注;但如何通过三维数字化原真再现民族舞蹈表演是极具挑战的问题.本论文通过动捕技术采集舞蹈动作实现舞蹈数字化展示.具体方法是:首先利用动捕设备捕获人体动作数据,然后在Maya中进行人物建模、骨骼绑定、蒙皮和权重调节,再通过MotionBuilder将3D模型与动捕数据结合,最终完成了现实舞蹈动作的虚拟人展演.论文构建了一个面向民族舞蹈展演的虚拟场景,并以13个民族的舞蹈为数字化内容,推广动捕驱动的舞蹈展演方法的应用.     

Development of a motion analysis system using acceleration sensors for tennis and its evaluations

The term biological motion is often used by researchers studying the patterns of movement generated by living forms and in sports. We studied a pattern recognition system of motion in sport using biological motion data. Biological motion data are acquired using a 3D motion capture system. However, 3D motion capture systems are very expensive. In this article, a biological motion capture system was built using acceleration sensors. Our proposed system uses the technique of Gaussian fitting and regression analysis. We tested our proposed system in pattern recognition of outdoor tennis and its evaluations.     

A compact smoothing-differentiation and projection approach for the kinematic data consistency of biomechanical systems

The estimation of the skeletal motion obtained from marker-based motion capture systems affects the results of the kinematic and dynamic analysis of biomechanical systems. The main source of error is the inaccuracy of velocities and accelerations derived from experimentally measured displacements of markers placed on the skin of joints. This error is mainly due to the amplification of high-frequency low-amplitude noise introduced by the motion capture system when the raw displacement signals are differentiated. Another source of error is the skin motion artifact that produces violations of the kinematic constraint equations of the multibody system. An integrated smoothing-differentiation-projection approach to ensure the kinematic data consistency in the context of the analysis of biomechanical systems is presented. The raw data differentiation problem is solved by applying a single-step smoothing-differentiation technique based on the Newmark integration scheme. A systematic multibody procedure is proposed based on the projection of the positions and its smoothed derivatives into their corresponding constraint manifolds to ensure the kinematic data consistency. Several benchmark kinematic signals that include an acquired nonstationary mono-dimensional motion of biomechanical origin and computer generated data of a four-bar mechanism were processed using the proposed method to study its performance.     

HDFS存储和优化技术研究综述

HDFS(Hadoop distributed file system)作为面向数据追加和读取优化的开源分布式文件系统,具备可移植、高容错和可大规模水平扩展的特性.经过10余年的发展,HDFS已经广泛应用于大数据的存储.作为存储海量数据的底层平台,HDFS存储了海量的结构化和非结构化数据,支撑着复杂查询分析、交互式分析、详单查询、Key-Value读写和迭代计算等丰富的应用场景.HDFS的性能问题将影响其上所有大数据系统和应用,因此,对HDFS存储性能的优化至关重要.介绍了HDFS的原理和特性,对已有HDFS的存储及优化技术,从文件逻辑结构、硬件设备和应用负载这3个维度进行了归纳和总结.综述了近年来HDFS存储和优化相关研究.未来,随着HDFS上层应用的日益丰富和底层硬件平台的发展,基于异构平台的数据存储、面向应用负载的自适应存储优化以及结合机器学习的存储优化技术将成为未来研究的主要方向.     

基于Visual C++和DCMTK的医学DICOM图像显示与调窗

随着影像技术的发展,医学影像在现代医学中扮演着重要的角色,而医学影像常以DI-COM文件格式存储。为方便DICOM图像处理,基于Visual C++和DCMTK免费开源开发包设计医学DICOM图像的读写和显示功能,实现DICOM图像的读取并根据选定的窗宽/窗位保存为BMP格式文件,同时在MFC下完成鼠标拖动进行窗宽/窗位的调节。     

Quantification of dance movement by simultaneous measurement of body motion and biophysical information

The purpose of this research is a quantitative analysis of movement patterns of dance,which cannot be analyzed with a motion capture system alone,using simultaneous measurement of body motion and biophysical information.In this research,two kinds of same leg movement are captured by simultaneous measurement;one is a leg movement with given strength,the other is a leg movement without strength on condition of basic experiment using optical motion capture and electromyography (EMG) equipment in order to quantitatively analyze characteristics of leg movement.Also,we measured the motion of the traditional Japanese dance using the constructed system.We can visualize leg movement of Japanese dance by displaying a 3D CG character animation with motion data and EMG data.In addition,we expect that our research will help dancers and researchers on dance through giving new information on dance movement which cannot be analyzed with only motion capture.     

基于QT、GDAL处理遥感影像GeoTIFF数据的应用

GeoTIFF图像格式可以存储各种地理信息,因此在遥感图像领域应用十分广泛.但随着遥感影像的数据量迅速增长,如何提高对海量影像数据调度及显示成为急需解决的问题.本文利用开源库GDAL实现了对海量遥感影像GeoTIFF格式文件的地理信息读取,通过建立图形金字塔的方式对影像数据进行快速索引,最后利用跨平台QT语言开发,实现GeoTIFF格式文件在各种不同操作系统下放大、缩小及全屏显示.     

基于数据库日志的变化数据捕获研究

变化数据捕获方法是数据集成基础设施的战略组成部分,不断推动ETL、EAI等技术的发展.许多数据库厂商都提供了自己的CDC(Change data capture)产品,但只限于针对本身的数据库系统,价格也比较昂贵.虽然通过扫描数据库日志文件可以捕获变化数据,但大多数数据库系统都不提供日志文件的内部格式而只是提供日志访问的程序接口,如Oracle,SQL Server和DB2等.这些提供的接口有的访问活动日志,有的访问稳定日志,有的访问归档日志,因此很难保证读取日志文件的可靠性.现有的研究主要是如何利用程序应用接口读取日志文件,忽略了对可靠性的分析.本文针对读取不同类型的日志文件的可靠性条件进行了分析,提出了可靠读取规则及读取算法;并提出了从日志文件中有效抽取变化数据算法,实验证明了可靠性分析模型.     

Smart Vidente: advances in mobile augmented reality for interactive visualization of underground infrastructure

Many civil engineering tasks require to access geospatial data in the field and reference the stored information to the real-world situation. Augmented reality (AR), which interactively overlays 3D graphical content directly over a view of the world, can be a useful tool to visualize but also create, edit and update geospatial data representing real-world artifacts. We present research results on the next-generation field information system for companies relying on geospatial data, providing mobile workforces with capabilities for on-site inspection and planning, data capture and as-built surveying. To achieve this aim, we used mobile AR technology for on-site surveying of geometric and semantic attributes of geospatial 3D models on the user’s handheld device. The interactive 3D visualizations automatically generated from production databases provide immediate visual feedback for many tasks and lead to a round-trip workflow where planned data are used as a basis for as-built surveying through manipulation of the planned data. Classically, surveying of geospatial objects is a typical scenario performed from utility companies on a daily basis. We demonstrate a mobile AR system that is capable of these operations and present first field trials with expert end users from utility companies. Our initial results show that the workflows of planning and surveying of geospatial objects benefit from our AR approach.     

基于网络环境的3D GIS空间数据模型研究

三维地理信息系统应该充分利用第三维,提供有效空间分析功能。论文提出了一种面向分析的空间三维数据模型,在这个模型上基于几何拓扑关系的空间分析得以方便的实现。每种模型有不同种表现形式。LOD算法、空间索引和渐进数据传输使这种模型在网络环境上传输得以实现。     

A web-based,interactive visualization tool for social environmental survey data

Understanding human motivations and actions related to environmental problems is central to modeling complex, human-natural systems. However, social science survey data on environmental issues are often presented in relatively static reports and figures and are not easily accessible for participatory deliberation. Federal data sharing mandates motivate innovative data visualization and sharing mechanisms. We developed an open-source, web-based Survey Data Viewer as a visual interface to explore quantitative social science survey data. We used the Python Django web framework and the D3.js visualization library to create and deploy the tool. The Viewer was implemented using a water-related survey administered to a large, random sample of Utah adults in public venues. The Viewer allows users to visualize question responses based on demographic variables with percentages and mean response levels. We developed a standardized template for encoding survey data and metadata that permits the generalization of the tool to other similar surveys.     

HandPuppet3D: Motion capture and analysis for character animation

Motion capture is a technique of digitally recording the movements of real entities, usually humans. It was originally developed as an analysis tool in biomechanics research, but has grown increasingly important as a source of motion data for computer animation. In this context it has been widely used for both cinema and video games. Hand motion capture and tracking in particular has received a lot of attention because of its critical role in the design of new Human Computer Interaction methods and gesture analysis. One of the main difficulties is the capture of human hand motion. This paper gives an overview of ongoing research “HandPuppet3D” being carried out in collaboration with an animation studio to employ computer vision techniques to develop a prototype desktop system and associated animation process that will allow an animator to control 3D character animation through the use of hand gestures. The eventual goal of the project is to support existing practice by providing a softer, more intuitive, user interface for the animator that improves the productivity of the animation workflow and the quality of the resulting animations. To help achieve this goal the focus has been placed on developing a prototype camera based desktop gesture capture system to capture hand gestures and interpret them in order to generate and control the animation of 3D character models. This will allow an animator to control 3D character animation through the capture and interpretation of hand gestures. Methods will be discussed for motion tracking and capture in 3D animation and in particular that of hand motion tracking and capture. HandPuppet3D aims to enable gesture capture with interpretation of the captured gestures and control of the target 3D animation software. This involves development and testing of a motion analysis system built from algorithms recently developed. We review current software and research methods available in this area and describe our current work.     

基于LabVIEW的脊柱生物力学测量系统研究

设计了一种能够模拟脊柱侧弯、前屈、后伸、轴向压缩等方向的运动、快速分析脊柱生物力学特性的测量系统。对脊柱不同方向的加载通过万能材料试验机结合特制夹具实现,椎骨的应变由应变式传感器测量。基于LabVIEW平台开发的应变记录模块通过RS—232读取应变信号并实时显示。数据综合处理模块对传感器所测的信号和试验机记录的载荷、位移数据进行整合分析,最终由数据分析模块得出反映脊柱应变、刚度、运动范围等生物力学特性的参数或曲线。实验结果表明:该测量系统操作简单,能够模拟脊柱的三维运动,实现对脊柱多个生物力学特征参数的快速测定。     

Capturing human motion using body‐fixed sensors: outdoor measurement and clinical applications

Motion capture is mainly based on standard systems using optic, magnetic or sonic technologies. In this paper, the possibility to detect useful human motion based on new techniques using different types of body‐fixed sensors is shown. In particular, a combination of accelerometers and angular rate sensors (gyroscopes) showed a promising design for a hybrid kinematic sensor measuring the 2D kinematics of a body segment. These sensors together with a portable datalogger, and using simple biomechanical models, allow capture of outdoor and long‐term movements and overcome some limitations of the standard motion capture systems. Significant parameters of body motion, such as nature of motion (postural transitions, trunk rotation, sitting, standing, lying, walking, jumping) and its spatio‐temporal features (velocity, displacement, angular rotation, cadence and duration) have been evaluated and compared to the camera‐based system. Based on these parameters, the paper outlines the possibility to monitor physical activity and to perform gait analysis in the daily environment, and reviews several clinical investigations related to fall risk in the elderly, quality of life, orthopaedic outcome and sport performance. Taking advantage of all the potential of these body‐fixed sensors should be promising for motion capture and particularly in environments not suitable for standard technology such as in any field activity. Copyright © 2004 John Wiley & Sons, Ltd.     

Discriminative semi-supervised learning of dynamical systems for motion estimation

We introduce novel discriminative semi-supervised learning algorithms for dynamical systems, and apply them to the problem of 3D human motion estimation. Our recent work on discriminative learning of dynamical systems has been proven to achieve superior performance than traditional generative learning approaches. However, one of the main issues of learning the dynamical systems is to gather labeled output sequences which are typically obtained from precise motion capture tools, hence expensive. In this paper we utilize a large amount of unlabeled (input) video data to improve the prediction performance of the dynamical systems significantly. We suggest two discriminative semi-supervised learning approaches that extend the well-known algorithms in static domains to the sequential, real-valued multivariate output domains: (i) self-training which we derive as coordinate ascent optimization of a proper discriminative objective over both model parameters and the unlabeled state sequences, (ii) minimum entropy approach which maximally reduces the model's uncertainty in state prediction for unlabeled data points. These approaches are shown to achieve significant improvement against the traditional generative semi-supervised learning methods. We demonstrate the benefits of our approaches on the 3D human motion estimation problems.     

Calculation and visualization of the dynamic ability of the human body

There is a great demand for data on the mobility and strength capability of the human body in many areas, such as ergonomics, medical engineering, biomechanical engineering, computer graphics (CG) and virtual reality (VR). This paper proposes a new method that enables the calculation of the maximal force exertable and acceleration performable by a human body during arbitrary motion. A musculoskeletal model of the legs is used for the calculation. Using our algorithm, it is possible to evaluate whether a given posture or motion is a feasible one. A tool to visualize the calculated maximal feasibility of each posture is developed. The obtained results can be used as criteria of manipulability or strength capability of the human body, important in ergonomics and human animation. Since our model is muscle‐based, it is possible to simulate and visualize biomechanical effects such as fatigue and muscle training. The solution is based on linear programming and the results can be obtained in real time. Copyright © 1999 John Wiley & Sons, Ltd.     

基于MEMS 传感器和Unity3D 的人体运动捕获系统

随着人机交互技术的发展,人与计算机之间自然、多模态交互将成为人与计算机 之间交互的主要方式,而这首先需要计算机可以正确地理解和捕捉人的行为特征,运动捕获技 术正是在这种背景下提出来。通过运动捕获技术,计算机可以理解人体动作,用户就可以通过 体态、方位、手势和表情等模态向计算机发出指令、传达信息等,因此运动捕获是新一代人机 交互中的关键技术之一。目前基于MEMS 惯性传感器的动作捕获研究主要针对动漫和电影制 作,价格昂贵;随着传感器的集成度变高,价格逐渐降低带来了新的系统设计需求。在原有动 作捕获技术基础上,设计并实现一种普适性更好的人体动作捕获系统原型,原型系统实现了基 于惯性传感节点的人体运动信息的采集与融合、节点与汇聚节点的数据传输、虚拟人体模型实 时动作呈现程序;实现了从数据测量到采集,再到模拟呈现的全过程。