基于运动分割和稀疏低秩分解的失真人体运动捕捉数据恢复

针对人体运动的复杂性和噪声干扰的无序性,提出一种基于运动分割和稀疏低秩分解的失真人体运动捕捉数据恢复方法.首先利用双边滤波对失真运动数据进行预修正,降低干扰数据的奇异信息并保证运动序列的连贯性;其次采用概率主元分析方法将修正后的运动数据进行语义行为自动分割,得到不同姿态的运动语义子区间;再利用加速近端梯度优化算法对每个失真运动子片段数据矩阵根据其更优低秩特性进行稀疏低秩分解,实现运动子片段数据的局部恢复;最后将局部恢复后的各子运动片段根据人体运动序列的时序特性组合,达到整体失真运动捕捉数据恢复的目的.实验结果表明,该方法能够有效地对失真人体运动数据进行恢复,效果显著,有助于重构逼近真实人体姿态的运动捕捉数据.     

Modeling Human Judgment of Digital Imagery for Multimedia Retrieval

The application of machine learning techniques to image and video search has been shown to boost the performance of multimedia retrieval systems, and promises to lead to more generalized semantic search approaches. In particular, the availability of large training collections allows model-driven search using a substantial number of semantic concepts. The training collections are obtained in a manual annotation process where human raters review images and assign predefined semantic concept labels. Besides being prone to human error, manual image annotation is biased by the view of the individual annotator because visual information almost always leaves room for ambiguity. Ideally, several independent judgments are obtained per image, and the inter-rater agreement is assessed. While disagreement between ratings bears valuable information on the annotation quality, it complicates the task of clearly classifying rated images based on multiple judgments. In the absence of a gold standard, evaluating multiple judgments and resolving disagreement between raters is not trivial. In this paper, we present an approach using latent structure analysis to solve this problem. We apply latent class modeling to the annotation data collected during the TRECVID 2005 Annotation Forum, and demonstrate how to use this statistic to clearly classify each image on the basis of varying numbers of ratings. We use latent class modeling to quantify the annotation quality and discuss the results in comparison with the well-known Kappa inter-rater agreement measure.     

人体运动仿真建模方法研究

为了研究人体在运动过程中的力学行为,探讨人体运动仿真的建模方法,运用多体系统动力学的建模方法建立了17刚体,55自由度的人体动力学模型,模型对人体颈部和下躯干的柔性效应给予了充分考虑,基于第一类Lagrange方程推导了系统的动力学方程,并以某人体为例,利用该模型对人体的行走过程进行了仿真计算,计算结果表明:采用所建立的人体动力学模型进行人体步态仿真、碰撞仿真等研究是现实可行的,最后结合对人体运动仿真的未来发展趋势的展望,说明人体动力学模型有待进一步完善.     

人体运动建模的实时逆运动学算法

人体运动的虚拟仿真及建模是当今计算机图形学研究的一个热点,而逆向运动学方法则是求解人体运动的一项关键技术.为了实现人体运动建模,提出一种新颖的基于人体关节约束的实时逆向运动学算法.首先基于骨骼长度改进了传统的循环坐标下降(CCD)算法,使其生成的运动效果更加流畅自然;然后引入生物分子运动模拟中的SHAKE算法,使短距离运动建模效果更加高效、鲁棒.在此基础上,提出了模拟多个节点同时运动的解析算法,以获得在多个关节点共同作用下的人体运动仿真效果.同运动捕捉的实测数据相比,采用文中算法所得的结果与在视觉效果上非常接近,并通过实验证实了其应用价值.     

基于概率PCA模型的三维人体运动分类

提出基于概率主成分分析的三维人体运动自动识别与分类算法.它根据不同类别的人体运动应各有自己代表性的特征集,采用概率PCA方法建立各类动作的高斯分布模型;然后构建基于最小错误率贝叶斯决策理论的多分类器,实现对未知的动作序列(或具有代表性的帧)进行多分类决策.该方法具有概率模型的优点,适合高维数据处理;同时,这种方法能够提取运动数据的内在特征,较好地消除了运动数据在时间轴上的差异带来的问题,从而准确地对运动数据进行分类,实验结果证明了本文方法的有效性.     

Sparse Inertial Poser: Automatic 3D Human Pose Estimation from Sparse IMUs

We address the problem of making human motion capture in the wild more practical by using a small set of inertial sensors attached to the body. Since the problem is heavily under‐constrained, previous methods either use a large number of sensors, which is intrusive, or they require additional video input. We take a different approach and constrain the problem by: (i) making use of a realistic statistical body model that includes anthropometric constraints and (ii) using a joint optimization framework to fit the model to orientation and acceleration measurements over multiple frames. The resulting tracker Sparse Inertial Poser (SIP) enables motion capture using only 6 sensors (attached to the wrists, lower legs, back and head) and works for arbitrary human motions. Experiments on the recently released TNT15 dataset show that, using the same number of sensors, SIP achieves higher accuracy than the dataset baseline without using any video data. We further demonstrate the effectiveness of SIP on newly recorded challenging motions in outdoor scenarios such as climbing or jumping over a wall.     

Modeling Complex Unfoliaged Trees from a Sparse Set of Images

We present a novel image‐based technique for modeling complex unfoliaged trees. Existing tree modeling tools either require capturing a large number of views for dense 3D reconstruction or rely on user inputs and botanic rules to synthesize natural‐looking tree geometry. In this paper, we focus on faithfully recovering real instead of realistically‐looking tree geometry from a sparse set of images. Our solution directly integrates 2D/3D tree topology as shape priors into the modeling process. For each input view, we first estimate a 2D skeleton graph from its matte image and then find a 2D skeleton tree from the graph by imposing tree topology. We develop a simple but effective technique for computing the optimal 3D skeleton tree most consistent with the 2D skeletons. For each edge in the 3D skeleton tree, we further apply volumetric reconstruction to recover its corresponding curved branch. Finally, we use piecewise cylinders to approximate each branch from the volumetric results. We demonstrate our framework on a variety of trees to illustrate the robustness and usefulness of our technique.     

捕捉与提取影像片断中的人物动作

该文详细分析了运用自适应最小二乘法捕捉和提取影像中人物动作行为的实验过程。并通过实验总结出线性预测,迭代调整,恢复丢失特征点等方法,并对自适应最小二乘法进行了改进。实验结果已经应用于新加坡南洋理工大学对影像中的个体动作进行动画复原的项目中,对于其它图像处理应用领域也有广阔的应用前景。     

虚拟人几何建模及运动控制方法的研究

对目前虚拟人几何模型建市的几种常用方法进行了阐述,并对它们的优缺点进行了对比分析,同时对走步、跑步等几种典型的运动的控制方法进行了概述和对比研究.     

运动控制建模

机械、电气以及软件工程的集成正在改变运动控制设计及其发展的面貌。基于自己在传统机器制造业中的经验,Rockwell Automation的Kinetix Motion Control Business产品市场经理John Pritchard说,"单独的机械、控制和电气设计小组进行独立的工作,生产整台机     

Predicting Articulated Human Motion from Spatial Processes

We present a probabilistic interpretation of inverse kinematics and extend it to sequential data. The resulting model is used to estimate articulated human motion in visual data. The approach allows us to express the prior temporal models in spatial limb coordinates, which is in contrast to most recent work where prior models are derived in terms of joint angles. This approach has several advantages. First of all, it allows us to construct motion models in low dimensional spaces, which makes motion estimation more robust. Secondly, as many types of motion are easily expressed in spatial coordinates, the approach allows us to construct high quality application specific motion models with little effort. Thirdly, the state space is a real vector space, which allows us to use off-the-shelf stochastic processes as motion models, which is rarely possible when working with joint angles. Fourthly, we avoid the problem of accumulated variance, where noise in one joint affects all joints further down the kinematic chains. All this combined allows us to more easily construct high quality motion models. In the evaluation, we show that an activity independent version of our model is superior to the corresponding state-of-the-art model. We also give examples of activity dependent models that would be hard to phrase directly in terms of joint angles.     

单目视频中无标记的人体运动跟踪

提出一种人体运动跟踪算法,从无关节标记的单目视频中获取人体运动,利用一个带外观模板的人体关节模型,通过学习得到的运动模型及基于外观模型的相似性计算,巧妙地利用粒子滤波的概率密度传播策略鲁棒地跟踪普通单目视频中的人体运动,当出现跟踪丢失时,能在后续序列中自动恢复正确跟踪,且能较好地处理遮挡和自遮挡问题,实验表明,该算法鲁棒性好,跟踪结果令人满意。     

Affine Approximation for Direct Batch Recovery of Euclidian Structure and Motion from Sparse Data

We present a batch method for recovering Euclidian camera motion from sparse image data. The main purpose of the algorithm is to recover the motion parameters using as much of the available information and as few computational steps as possible. The algorithm thus places itself in the gap between factorisation schemes, which make use of all available information in the initial recovery step, and sequential approaches which are able to handle sparseness in the image data. Euclidian camera matrices are approximated via the affine camera model, thus making the recovery direct in the sense that no intermediate projective reconstruction is made. Using a little known closure constraint, the F_A-closure, we are able to formulate the camera coefficients linearly in the entries of the affine fundamental matrices. The novelty of the presented work is twofold: Firstly the presented formulation allows for a particularly good conditioning of the estimation of the initial motion parameters but also for an unprecedented diversity in the choice of possible regularisation terms. Secondly, the new autocalibration scheme presented here is in practice guaranteed to yield a Least Squares Estimate of the calibration parameters. As a bi-product, the affine camera model is rehabilitated as a useful model for most cameras and scene configurations, e.g. wide angle lenses observing a scene at close range. Experiments on real and synthetic data demonstrate the ability to reconstruct scenes which are very problematic for previous structure from motion techniques due to local ambiguities and error accumulation.     

Viewpoint Selection for Human Actions

In many scenarios a dynamic scene is filmed by multiple video cameras located at different viewing positions. Visualizing such multi-view data on a single display raises an immediate question—which cameras capture better views of the scene? Typically, (e.g. in TV broadcasts) a human producer manually selects the best view. In this paper we wish to automate this process by evaluating the quality of a view, captured by every single camera. We regard human actions as three-dimensional shapes induced by their silhouettes in the space-time volume. The quality of a view is then evaluated based on features of the space-time shape, which correspond with limb visibility. Resting on these features, two view quality approaches are proposed. One is generic while the other can be trained to fit any preferred action recognition method. Our experiments show that the proposed view selection provide intuitive results which match common conventions. We further show that it improves action recognition results.     

由正交图像造型三维个性化虚拟人体模型

基于正、侧、背面4幅正交人体图像,变形模板人体模型后得到带服饰纹理的个性化虚拟人．以截面环为基本几何元素,给出了模板人体模型的生成和变形方法,建立了截面环和三维人体骨架之间的内在关联;自动提取图像上的人体特征点,生成二维图像上的人体骨架;由三维人体骨架和二维图像上人体骨架之间的对应关系,建立截面环和人体图像之间的对应关系;从人体图像上获取截面环的形状参数,通过参数化变形截面环得到个性化的虚拟人体模型．与已有方法相比,该方法建立在基于特征的参数化框架之上,变形更容易控制,消除了三维模型和二维图像之间对应的不确定性,避免了人体变形中的自交现象,效率高、真实感效果较好．     

基于彩色聚类和人体运动知识的人体运动理解

提出了利用人体区域彩色聚类和人体运动的相关知识,理解机器视觉中人体运动的方法。根据人类视觉区分颜色的特性和实时视觉图像,自动调整区域颜色聚类的阈值;对视觉图像中的人体进行区域聚类,并分别计算出各区域聚类中心和聚类区域的质心;根据视觉图像中前后帧相对应的聚类区域质心位置变化,利用人体运动与聚类区域质心位移关系的知识,从整体或细节上理解人体的运动。     

Multi-level Motion Attention for Human Motion Prediction

International Journal of Computer Vision - Human motion prediction aims to forecast future human poses given a historical motion. Whether based on recurrent or feed-forward neural networks,...     

Shape-From-Silhouette Across Time Part II: Applications to Human Modeling and Markerless Motion Tracking

In Part I of this paper we developed the theory and algorithms for performing Shape-From-Silhouette (SFS) across time. In this second part, we show how our temporal SFS algorithms can be used in the applications of human modeling and markerless motion tracking. First we build a system to acquire human kinematic models consisting of precise shape (constructed using the temporal SFS algorithm for rigid objects), joint locations, and body part segmentation (estimated using the temporal SFS algorithm for articulated objects). Once the kinematic models have been built, we show how they can be used to track the motion of the person in new video sequences. This marker-less tracking algorithm is based on the Visual Hull alignment algorithm used in both temporal SFS algorithms and utilizes both geometric (silhouette) and photometric (color) information.Electronic supplementary material Electronic supplementary material is available for this article at and accessible for authorised users.