运动捕捉数据处理、检索与重构方法研究
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摘要
人体运动捕捉是近年来新兴的一种数据采集方式,是数学、计算机图形学、图像处理、数据处理等多个学科相互渗透、相互交叉而产生的新兴研究领域,引起了国内外众多研究人员的注意,大量的研究成果不断涌现。人体运动捕捉的研究不仅具有一定的理论研究意义,还具有广泛的实际应用价值。到目前为止,人体运动捕捉已经广泛应用于现代影视动画、游戏制作、医学分析、体育科学研究和运动训练指导等领域。
在运动捕捉逐渐发展的近二十年里,国内外很多学者和研究人员在相关领域开展了研究工作,在许多方面都取得了丰富的理论和实践成果,包括运动捕捉方式的研究,运动捕捉设备的设计、开发以及系统架构,运动捕捉数据处理算法及相关软件系统开发,运动捕捉数据的管理、压缩、检索与重用方法,运动数据的理解及人体运动分析,人体运动重构方法等。本文的主要研究工作包括以下几个方面:
1)光学运动捕捉散乱数据处理方法:针对运动捕捉系统获取的三维散乱中间数据,提出了基于结构匹配的运动捕捉数据处理方法和基于语义块的动态脉冲噪声模型实现噪声数据和缺失数据的处理。根据目前我国运动捕捉发展现状,结合企业实际问题,提出的方法基于人体的刚性结构假设,通过五种匹配类型和拓扑结构校验的方法实现三维空间散乱数据点的识别、匹配与重构。在此基础上,通过定义语义节点,提出语义块和备选语义块的概念,基于语义块而非传统的特征点轨迹构建动态噪声模型,进而应用中值滤波算法实现噪声数据的滤除和缺失数据修复。在以上两种方法的理论基础上,设计开发数据处理系统并进行实验验证,实验结果表明以上方法具有较好的数据处理效果及鲁棒性和较高的处理效率。
2)人体运动特征表示与运动捕捉数据检索方法:提出基于语义模型的人体运动高维空间特征提取方法,引入仿生模式识别理论并实现基于该理论的人体运动数据检索方法。针对运动捕捉数据特征提取与检索问题,定义语义节点、建立人体语义模型,测试了基于语义相似度的检索方法。此外,基于构造的高维空间特征向量,根据检索样本在高维空间中构造同类型运动数据的特征覆盖域,从而实现检索目的。进而在此基础上,引入仿生模式识别理论,在高维空间中构造改进的HSN神经元表示同类型运动在高维空间中的特征覆盖域,以实现运动数据的检索。在设计的原型系统中,测试数据库采用卡耐基梅隆大学(CMU)运动捕捉实验室的开放式运动捕捉数据库进行验证,实验结果表明以上方法具有较高的检索精度和检索效率。
3)基于数据驱动的人体及人脸运动重构方法:将运动捕捉数据与非均匀有理B样条(NURBS)方法相结合,分别提出局部坐标系插补方法和NURBS映射方法,实现了运动捕捉数据驱动的NURBS人体和人脸模型的运动重构动态仿真。针对人体运动,首先定义一个分片的基于NURBS曲面的人体虚拟模型,进而设定曲面控制点与语义节点的关联,根据从运动捕捉数据驱动过程中提取的语义模型的变化,通过坐标系插补的方法驱动曲面的变形以实现人体运动动态模拟。此外,特别针对面部运动,构造基于NURBS曲面和MPEG-4标准的虚拟人脸模型,通过对运动捕捉数据的局部坐标系分解,分别提取出面部表情运动与头部整体运动,进而建立运动捕捉数据与NURBS控制点的映射关联,实现面部表情的仿真。最后,分别设计开发原型系统并验证了方法的有效性。
综上所述,本文主要围绕以上三个方面提出了有效的处理方法和实现途径,并在算法基础上,分别设计开发了相应的软件或原型系统,在大量实验数据测试的基础上,验证了方法的有效性、鲁棒性和执行效率。为设计和开发自主知识产权的运动捕捉数据处理软件系统提供了算法基础和可行性分析依据。
Human motion capture is a rising technique for data acquisition and it is a multi-disciplinary intersection of mathematics,computer graphics,image processing,data processing and so on.In the past decades,many researchers at home and abroad focused on motion capture and obtained lots of fruitful achievements.The technique has been widely used in modern computer animation,film making,virtual game,medical analysis and physical training.The study on motion capture is an important issue with not only theoretical significance but also application value.
Since 1990's,with the development of the motion capture,many fruitful results can be found in solutions to various practical problems such as research in capture pattern,design and development of motion capture system,data processing algorithms and relevant software, management and retrieval methods for motion data reuse,comprehension and analysis of motion data,motion reconstruction and so on.The main works of this thesis includes three aspects as follows:
1.Scattered data processing for optical motion capture:a data processing method based on rigid structure matching and a dynamic impulse noise model based on semantic parts for noise and missing data processing are presented.According to the development and practical problems of internal industry,the proposed rigid structure matching-based method resolves the problems of recognition,matching and reconstruction of scattered makers by five matching types and topological checking on the hypothesis of articulated rigid structure of human body.Furthermore,by the definition of semantic joint,semantic part and candidate part,the dynamic impulse noise model constructed based on semantic parts rather than traditional trajectories of markers filters the noise data and reconstructs the missing markers by use of median filter algorithm.Finally,data processing system is developed on the basis of algorithm and experiments show that the approaches can resolve the problems with better results,robustness and efficiency.
2.Feature representation of human motion and motion data retrieval:feature representation and extraction in high dimension space for human motion are proposed,and then a motion data retrieval algorithm is implemented based on biomimetic pattern recognition.To resolve the problem of feature representation extraction and retrieval of human motion,semantic joint and semantic model are defined,a semantic similarity approach is examined.Moreover,by feature extraction,a method of hyper-sphere cover in high dimension space is presented with evaluation of the cover domain of same type motions' feature vectors.Furthermore,based on biomimetic pattern recognition,improved hyper sausage neurons and hyper sausage neuron chain in high dimension space are constructed for same type motions to cover the feature domain for motion data retrieval.The experimental system examines the proposed algorithms with the CMU free motion database,and results show that the methods achieve better retrieval recall and precision with satisfied efficiency.
3.Data driven approaches for human body and facial motion reconstruction:by the combination of motion capture data and NURBS,two data driven approaches for human body and facial motion reconstruction are implemented respectively based on local coordinate interpolation and NURBS mapping.For human body motion simulation,a piece-wise NURBS based human body model is constructed and the relationship between NURBS control points and semantic joints are determined subsequently.Then,the morph of human model is driven based on local coordinate interpolation algorithm by semantic model which extracted from motion data.Especially for facial motion,a MPEG-4 compatible NURBS surface facial model is constructed and then the morph of facial model is driven by motion data using local coordinate decomposition and NURBS mapping.The simulation feasibility and efficiency are proved by experimental systems.
To sum up,efficient and practicable data processing methods are proposed,and experimental systems are developed.The robustness and efficiency of methods are examined based on mass experimental data.The study of these problems provides the arithmetic and feasible basis for design and development of software with independent intellectual property.
在运动捕捉逐渐发展的近二十年里,国内外很多学者和研究人员在相关领域开展了研究工作,在许多方面都取得了丰富的理论和实践成果,包括运动捕捉方式的研究,运动捕捉设备的设计、开发以及系统架构,运动捕捉数据处理算法及相关软件系统开发,运动捕捉数据的管理、压缩、检索与重用方法,运动数据的理解及人体运动分析,人体运动重构方法等。本文的主要研究工作包括以下几个方面:
1)光学运动捕捉散乱数据处理方法:针对运动捕捉系统获取的三维散乱中间数据,提出了基于结构匹配的运动捕捉数据处理方法和基于语义块的动态脉冲噪声模型实现噪声数据和缺失数据的处理。根据目前我国运动捕捉发展现状,结合企业实际问题,提出的方法基于人体的刚性结构假设,通过五种匹配类型和拓扑结构校验的方法实现三维空间散乱数据点的识别、匹配与重构。在此基础上,通过定义语义节点,提出语义块和备选语义块的概念,基于语义块而非传统的特征点轨迹构建动态噪声模型,进而应用中值滤波算法实现噪声数据的滤除和缺失数据修复。在以上两种方法的理论基础上,设计开发数据处理系统并进行实验验证,实验结果表明以上方法具有较好的数据处理效果及鲁棒性和较高的处理效率。
2)人体运动特征表示与运动捕捉数据检索方法:提出基于语义模型的人体运动高维空间特征提取方法,引入仿生模式识别理论并实现基于该理论的人体运动数据检索方法。针对运动捕捉数据特征提取与检索问题,定义语义节点、建立人体语义模型,测试了基于语义相似度的检索方法。此外,基于构造的高维空间特征向量,根据检索样本在高维空间中构造同类型运动数据的特征覆盖域,从而实现检索目的。进而在此基础上,引入仿生模式识别理论,在高维空间中构造改进的HSN神经元表示同类型运动在高维空间中的特征覆盖域,以实现运动数据的检索。在设计的原型系统中,测试数据库采用卡耐基梅隆大学(CMU)运动捕捉实验室的开放式运动捕捉数据库进行验证,实验结果表明以上方法具有较高的检索精度和检索效率。
3)基于数据驱动的人体及人脸运动重构方法:将运动捕捉数据与非均匀有理B样条(NURBS)方法相结合,分别提出局部坐标系插补方法和NURBS映射方法,实现了运动捕捉数据驱动的NURBS人体和人脸模型的运动重构动态仿真。针对人体运动,首先定义一个分片的基于NURBS曲面的人体虚拟模型,进而设定曲面控制点与语义节点的关联,根据从运动捕捉数据驱动过程中提取的语义模型的变化,通过坐标系插补的方法驱动曲面的变形以实现人体运动动态模拟。此外,特别针对面部运动,构造基于NURBS曲面和MPEG-4标准的虚拟人脸模型,通过对运动捕捉数据的局部坐标系分解,分别提取出面部表情运动与头部整体运动,进而建立运动捕捉数据与NURBS控制点的映射关联,实现面部表情的仿真。最后,分别设计开发原型系统并验证了方法的有效性。
综上所述,本文主要围绕以上三个方面提出了有效的处理方法和实现途径,并在算法基础上,分别设计开发了相应的软件或原型系统,在大量实验数据测试的基础上,验证了方法的有效性、鲁棒性和执行效率。为设计和开发自主知识产权的运动捕捉数据处理软件系统提供了算法基础和可行性分析依据。
Human motion capture is a rising technique for data acquisition and it is a multi-disciplinary intersection of mathematics,computer graphics,image processing,data processing and so on.In the past decades,many researchers at home and abroad focused on motion capture and obtained lots of fruitful achievements.The technique has been widely used in modern computer animation,film making,virtual game,medical analysis and physical training.The study on motion capture is an important issue with not only theoretical significance but also application value.
Since 1990's,with the development of the motion capture,many fruitful results can be found in solutions to various practical problems such as research in capture pattern,design and development of motion capture system,data processing algorithms and relevant software, management and retrieval methods for motion data reuse,comprehension and analysis of motion data,motion reconstruction and so on.The main works of this thesis includes three aspects as follows:
1.Scattered data processing for optical motion capture:a data processing method based on rigid structure matching and a dynamic impulse noise model based on semantic parts for noise and missing data processing are presented.According to the development and practical problems of internal industry,the proposed rigid structure matching-based method resolves the problems of recognition,matching and reconstruction of scattered makers by five matching types and topological checking on the hypothesis of articulated rigid structure of human body.Furthermore,by the definition of semantic joint,semantic part and candidate part,the dynamic impulse noise model constructed based on semantic parts rather than traditional trajectories of markers filters the noise data and reconstructs the missing markers by use of median filter algorithm.Finally,data processing system is developed on the basis of algorithm and experiments show that the approaches can resolve the problems with better results,robustness and efficiency.
2.Feature representation of human motion and motion data retrieval:feature representation and extraction in high dimension space for human motion are proposed,and then a motion data retrieval algorithm is implemented based on biomimetic pattern recognition.To resolve the problem of feature representation extraction and retrieval of human motion,semantic joint and semantic model are defined,a semantic similarity approach is examined.Moreover,by feature extraction,a method of hyper-sphere cover in high dimension space is presented with evaluation of the cover domain of same type motions' feature vectors.Furthermore,based on biomimetic pattern recognition,improved hyper sausage neurons and hyper sausage neuron chain in high dimension space are constructed for same type motions to cover the feature domain for motion data retrieval.The experimental system examines the proposed algorithms with the CMU free motion database,and results show that the methods achieve better retrieval recall and precision with satisfied efficiency.
3.Data driven approaches for human body and facial motion reconstruction:by the combination of motion capture data and NURBS,two data driven approaches for human body and facial motion reconstruction are implemented respectively based on local coordinate interpolation and NURBS mapping.For human body motion simulation,a piece-wise NURBS based human body model is constructed and the relationship between NURBS control points and semantic joints are determined subsequently.Then,the morph of human model is driven based on local coordinate interpolation algorithm by semantic model which extracted from motion data.Especially for facial motion,a MPEG-4 compatible NURBS surface facial model is constructed and then the morph of facial model is driven by motion data using local coordinate decomposition and NURBS mapping.The simulation feasibility and efficiency are proved by experimental systems.
To sum up,efficient and practicable data processing methods are proposed,and experimental systems are developed.The robustness and efficiency of methods are examined based on mass experimental data.The study of these problems provides the arithmetic and feasible basis for design and development of software with independent intellectual property.
引文
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