Animation toolkit based on a database approach for reusing motions and models

As animations become more readily available, simultaneously the complexity of creating animations has also increased. In this paper, we address the issue by describing an animation toolkit based on a database approach for reusing geometric animation models and their motion sequences. The aim of our approach is to create a framework aimed for novice animators. Here, we use an alternative notion of a VRML scene graph to describe a geometric model, specifically intended for reuse. We represent this scene graph model as a relational database. A set of spatial, temporal, and motion operations are then used to manipulate the models and motions in an animation database. Spatial operations help in inserting/deleting geometric models in a new animation scene. Temporal and motion operations help in generating animation sequences in a variety of ways. For instance, motion information of one geometric model can be applied to another model or a motion sequence can be retargeted to meet additional constraints (e.g., wiping action on a table can be retargeted with constraints that reduce the size of the table). We present the design and implementation of this toolkit along with several interesting examples of animation sequences that can be generated using this toolkit.     

Review of techniques for motion capture data processing

In order to high reality and efficiency,the technique of motion capture (MoCap) has been widely used in the field of computer animation.With the development of motion capture,a large amount of motion capture databases are available and this is significant for the reuse of motion data.But due to the high degree of freedoms and high capture frequency,the dimension of the motion capture data is usually very high and this will lead to a low efficiency in data processing.So how to process the high dimension data and design an efficient and effective retrieval approach has become a challenge which we can’t ignore.In this paper,first we lay out some problems about the key techniques in motion capture data processing.Then the existing approaches are analyzed and summarized.At last,some future work is proposed.     

一种新的光学运动捕捉数据处理方法*

提出一种适用于被动式光学人体运动捕捉散乱数据处理方法。该方法基于光学人体运动捕捉散乱数据的全局信息,提出基于模块分段线性模型的数据处理算法。利用模块分段线性模型归纳出不同模块的变化特征,从而确定各模块数据的匹配优先级及段内拟合函数,有效地对三维运动数据各模块进行全局性分层次预测和跟踪,并对噪声数据进行基于模块的去噪处理;对缺失运动数据提出基于分段Newton插值拟合算法,进行合理的补缺。该方法经优化后在处理过程中无须人工干预,并能满足实时性要求。     

Multiresolution coding of motion capture data for real-time multimedia applications

Multimedia Tools and Applications - In this work, we present a novel and efficient method for coding of motion capture (MoCap) data obtained from recording of human actions. MoCap data is...     

A recognition-based motion capture baseline on the HumanEva II test data

The advent of the HumanEva standardized motion capture data sets has enabled quantitative evaluation of motion capture algorithms on comparable terms. This paper measures the performance of an existing monocular recognition-based pose recovery algorithm on select HumanEva data, including all the HumanEva II clips. The method uses a physically motivated Markov process to connect adjacent frames and achieve a 3D relative mean error of 8.9 cm per joint. It further investigates factors contributing to the error and finds that research into better pose retrieval methods offers promise for improvement of this technique and those related to it. Finally, it investigates the effects of local search optimization with the same recognition-based algorithm and finds no significant deterioration in the results, indicating that processing speed can be largely independent of the size of the recognition library for this approach.     

Effective and efficient similarity searching in motion capture data

Multimedia Tools and Applications - Motion capture data describe human movements in the form of spatio-temporal trajectories of skeleton joints. Intelligent management of such complex data is a...     

Searching for variable-speed motions in long sequences of motion capture data

Motion capture data digitally represent human movements by sequences of body configurations in time. Subsequence searching in long sequences of such spatio-temporal data is difficult as query-relevant motions can vary in execution speeds and styles and can occur anywhere in a very long data sequence. To deal with these problems, we employ a fast and effective similarity measure that is elastic. The property of elasticity enables matching of two overlapping but slightly misaligned subsequences with a high confidence. Based on the elasticity, the long data sequence is partitioned into overlapping segments that are organized in multiple levels. The number of levels and sizes of overlaps are optimized to generate a modest number of segments while being able to trace an arbitrary query. In a retrieval phase, a query is always represented as a single segment and fast matched against segments within a relevant level without any costly post-processing. Moreover, visiting adjacent levels makes possible subsequence searching of time-warped (i.e., faster or slower executed) queries. To efficiently search on a large scale, segment features can be binarized and segmentation levels independently indexed. We experimentally demonstrate effectiveness and efficiency of the proposed approach for subsequence searching on a real-life dataset.     

基于运动捕获数据的虚拟人动画研究

随着三维游戏等行业对计算机动画制作需求的增加,在三维动画制作软件中人工调整虚拟人动作的工作方式已经不再适合现在的计算机动画制作。运动捕获技术是直接记录物体的运动数据并将其用于生成计算机动画,具有高效率、所生成的动画真实感强等优点,因而获得了广泛应用。提出了一种利用运动捕获数据来生成动画的方法,基于运动捕获得到的数据建立并驱动三维骨架模型,从而产生骨架的运动,形成动画。该方法可以充分利用现有的大量运动捕获数据,因此具有较大的应用前景。     

基于PGA的人体运动捕获数据分割方法

由于人体运动捕获数据的固有非线性,线性方法并不总是能够有效地找到运动捕获数据的内在维度,针对这种情况,提出了基于主测地线分析（PGA）和概率主测地线分析（PPGA）的自动分割方法。这两种方法都将人体运动视为一个有序的姿势序列,并在姿势序列有局部变化处对运动进行分割。基于PGA的分割方法是在运动局部模型的内在维度突然增长处分配一个分割点,基于PPGA的分割方法是在姿势分布发生改变时放置分割点。实验结果表明,该方法都能实现自动分割,且具有较好的分割结果。     

Segmentation and recognition of motion capture data stream by classification

Three dimensional human motions recorded by motion capture and hand gestures recorded by using data gloves generate variable-length data streams. These data streams usually have dozens of attributes, and have different variations for similar motions. To segment and recognize motion streams, a classification-based approach is proposed in this paper. Classification feature vectors are extracted by utilizing singular value decompositions (SVD) of motion data. The extracted feature vectors capture the dominating geometric structures of motion data as revealed by SVD. Multi-class support vector machine (SVM) classifiers with class probability estimates are explored for classifying the feature vectors in order to segment and recognize motion streams. Experiments show that the proposed approach can find patterns in motion data streams with high accuracy.

A novel clustering algorithm based on data transformation approaches

Clustering provides a knowledge acquisition method for intelligent systems. This paper proposes a novel data-clustering algorithm, by combining a new initialization technique, K-means algorithm and a new gradual data transformation approach to provide more accurate clustering results than the K-means algorithm and its variants by increasing the clusters’ coherence. The proposed data transformation approach solves the problem of generating empty clusters, which frequently occurs for other clustering algorithms. An efficient method based on the principal component transformation and a modified silhouette algorithm is also proposed in this paper to determine the number of clusters. Several different data sets are used to evaluate the efficacy of the proposed method to deal with the empty cluster generation problem and its accuracy and computational performance in comparison with other K-means based initialization techniques and clustering methods. The developed estimation method for determining the number of clusters is also evaluated and compared with other estimation algorithms. Significances of the proposed method include addressing the limitations of the K-means based clustering and improving the accuracy of clustering as an important method in the field of data mining and expert systems. Application of the proposed method for the knowledge acquisition in time series data such as wind, solar, electric load and stock market provides a pre-processing tool to select the most appropriate data to feed in neural networks or other estimators in use for forecasting such time series. In addition, utilization of the knowledge discovered by the proposed K-means clustering to develop rule based expert systems is one of the main impacts of the proposed method.     

A system for the capture and synthesis of insect motion

We present an integrated system that enables the capture and synthesis of 3D motions of small scale dynamic creatures, typically insects and arachnids, in order to drive computer generated models. The system consists of a number of stages, initially, the acquisition of a multi-view calibration scene and synchronised video footage of a subject performing some action is carried out. A user guided labelling process, that can be semi-automated using tracking techniques and a 3D point generating algorithm, then enables a full metric calibration and captures the motions of specific points on the subject. The 3D motions extracted, which often come from a limited number of frames of the original footage, are then extended to generate potentially infinitely long, characteristic motion sequences for multiple similar subjects. Finally a novel path following algorithm is used to find optimal path along with coherent motion for synthetic subjects.     

Human motion capture data retrieval based on semantic thumbnail

We present a method for the efficient retrieval and browsing of immense amounts of realistic 3D human body motion capture data. The proposed method organizes motion capture data based on statistical K-means (SK–means), democratic decision making, unsupervised learning, and visual key frame extraction, thus achieving intuitive retrieval by browsing thumbnails of semantic key frames. We apply three steps for the efficient retrieval of motion capture data. The first is obtaining the basic type clusters by clustering motion capture data using the novel SK-means algorithm, and after which, immediately performing character matching. The second is learning the retrieval information of users during the retrieval process and updating the successful retrieval rate of each data; the search results are then ranked on the basis of successful retrieval rate by democratic decision making to improve accuracy. The last step is generating thumbnails with semantic generalization, which is conducted by using a novel key frame extraction algorithm based on visualized data analysis. The experiment demonstrates that this method can be utilised for the efficient organization and retrieval of enormous motion capture data.     

运动捕捉数据中足迹的谱聚类检测方法

目的 从运动捕捉数据中直接检测足迹非常困难。目前,已有的方法不能在无人工交互条件下实现对任意平地运动数据的足迹自动检测,为此提出一种基于谱聚类的足迹自动检测方法,可应用于任意角色的平地运动。方法 首先,提取角色的脚部运动特征并表示为样本。然后,分析样本模长的变化规律并自适应计算谱聚类参数。最后,使用谱聚类方法检测出足迹帧。结果 实验应用于混合运动数据集后,足迹检测的准确性良好,检测的整体准确率可达98.72%。结论 对实验结果的分析以及与基准线法的比较,证明了本文方法的普适性和有效性。     

基于隐空间的运动捕获数据自动分割*

提出一种运动捕获数据自动分割方法。利用高斯过程隐变量模型对运动捕获数据进行降维,将其从高维观察空间映射到低维隐空间;在隐空间中构造运动特征函数,该函数具有结构简单、对所有关节敏感等优点,通过分析运动特征函数几何特征的变化,探测运动捕获数据的分割点,实现运动自动分割。实验结果表明,该方法具有较高的准确度和较好的普适性。     

Rate‐distortion optimized compression of motion capture data

Lossy compression of motion capture data can alleviate the problems of efficient storage and transmission by exploiting the redundancy and the superfluous precision of the data. When considering the acceptable amount of distortion, perceptual issues have to be taken into account. Current state of the art methods reduce the data rate required for high quality storage of motion capture data using various techniques. Most of them, however, do not use the common tools of general data compression, such as the method of Lagrange multipliers, and thus they obtain sub‐optimal results, making it difficult to do a fair comparison of their performance. In this paper, we present a general preprocessing step based on Lagrange multipliers, which allows to rigorously adjust the precision in each of the degrees of freedom of the input data according to the amount of influence the given degree of freedom has on the overall distortion. We then present a simple compression method based on Principal Component Analysis, which in combination with the proposed preprocessing achieves significantly better results than current state of the art methods. It allows optimization with respect to various distortion metrics, and we discuss the choice of the metric in two common but distinct scenarios, proposing a perceptually oriented comparison metric based on the relation of the problem at hand to the problem of compression of dynamic meshes.     

A body model server for human motion capture and representation

This paper presents a body model server (BMS) that provides real-time access to the position and posture of a person's torso, arms, hands, head, and eyes. It can be accessed by clients over a network. The BMS is designed to function as a device-independent data-layer between the sensing devices and client applications that require real-time human motion data, such as animation control. It can provide clients with accurate information at up to 40 Hz. For data collection, the model uses four magnetic position/orientation sensors, two data-gloves, and an eye-tracker. The BMS combines the data-streams from the sensors and transforms them into snapshots of the user's upper-body pose. A geometric model made up of joints and segments structures the input. Posture of the body is represented by joint angles. Two unique characteristics of our approach are the use of the implicit, geometric constraints of the sensed body to simplify the computation of the unmeasured joint angles, and the use of time-stamped data that allow synchronization with other data streams, e.g., speech input. This paper describes the architecture of the BMS, including the management of multiple input devices, the representation and computation of the position and joint angle data, and the client-server interface.     

Human motion capture data compression by model-based indexing: a power aware approach

Human motion capture (MoCap) data can be used for animation of virtual human-like characters in distributed virtual reality applications and networked games. MoCap data compressed using the standard MPEG-4 encoding pipeline comprising of predictive encoding (and/or DCT decorrelation), quantization, and arithmetic/Huffman encoding, entails significant power consumption for the purpose of decompression. In this paper, we propose a novel algorithm for compression of MoCap data, which is based on smart indexing of the MoCap data by exploiting structural information derived from the skeletal virtual human model. The indexing algorithm can be fine-controlled using three predefined quality control parameters (QCPs). We demonstrate how an efficient combination of the three QCPs results in a lower network bandwidth requirement and reduced power consumption for data decompression at the client end when compared to standard MPEG-4 compression. Since the proposed algorithm exploits structural information derived from the skeletal virtual human model, it is observed to result in virtual human animation of visually acceptable quality upon decompression