个性化人体模型与肢体协同运动研究
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摘要
近年来,三维个性化人体生成和肢体的协同运动已经成为虚拟现实领域的重要发展方向,作为三维虚拟服装CAD系统核心的组成部分,它允许用户直接对服装的款式和尺寸进行选择和修改,并通过三维显示的方式呈现给用户。另外,不同人体姿态的三维重构也会给服装的动态展示和舒适性评价打下坚实的基础。个性化生成和肢体协同运动后的三维人体模型能否准确反映人体的体型指标和姿态参数取决于生成原理和重构方法。因此,对个性化人体生成和肢体协同运动理论与方法的研究对于加快虚拟现实和三维服装CAD技术的发展有着重要的理论意义和应用价值。
三维个性化人体生成和肢体协同运动包括人体模型数据库的建立、个性化人体生成、线状骨架提取和人体姿态的三维重构理论与方法,整体目标在于构建面向互联网的用户服装个性化定制和展示平台,并在此基础上实现相关算法的研究。
提出基于三维图元的多方向协同变形的不同体型生成方法。首先通过现有模型库选择所使用的模板人体模型和目标人体模型,随后利用人体造型设计软件对模型进行了三维图元划分；利用不同图元的共性,抽象得到三维图元的各种参数,提取获得了模板图元的形状特征和目标图元的变化范围,提出图元曲线插值算法以生成图元方向截线；进而提出了最小距离准则的方法得到人体的不同图元,从而获得准确的不同人体体型。
提出基于三维图元和多方向轮廓线的个性化人体生成方法。阐述了参照法提取图像像素尺寸的原理和方法；根据不同方向的人体图像轮廓线；将部分分块和人体骨架相结合,提取图元方向尺寸；通过身高和图元方向尺寸分级匹配对人体图元数据库进行搜索,以确定相似的人体图元,从而为模型的变形提供依据；利用定位的人体轮廓线和关键特征点,提出了多方向轮廓线驱动变形的方法,生成了具有目标体型特征的三维人体模型。
提出基于改进Hopfield神经网络和方向特征的人体线状骨架提取方法。根据人体模型冠状面的定义,针对模型三角片提出了深度信息的概念,用以定位人体的躯干部位；提出了改进Hopfield神经网络和方向特征相结合的特征点定位方法,实现目标人体的躯干特征点定位；介绍了模型的斜截线原理,并提出利用肢体方向特征获取准确的人体肢体特征点的方法；基于人体的骨架模型,给出了准确的目标人体线状骨架。
提出基于共形变换的人体肢体的协同运动方法。提出了颜色直方图和距离分类相结合的方法,实现了对目标人体关节特征点的定位；阐述了人体骨架点和骨架段模型,并通过相互连接骨架点迭代计算尺度因子的方法,得到了骨架点的三维信息；在对运动方向与角度进行计算的基础上,提出了基于共形变换的人体肢体的协同运动方法,从而实现了人体肢体的协同运动以及人体姿态的三维重构。
最后,对三维个性化人体生成和肢体的协同运动两个模块进行了实现,并给出了图元方向截线的生成、基于图像的人体轮廓线提取、三维个性化人体生成、三维人体线状骨架提取和重构三维人体姿态等的实例,进一步验证了所提出的方法的正确性和有效性。
In recent years, the generation of3D personalized human body and the cooperative motion of human limb have become the important development direction for the Virtual Reality field. As a core part of3D virtual garment CAD system, it allows user to select and modify the styles and sizes of garments, and presents to the user by using the method of3D display. In addition,3D reconstruction of different human poses will lay a solid foundation for dynamic display and comfort evaluation on garments. If the3D human models obtained by the method of personalized generation and cooperative motion of limb can accurately reflect shape indicators and posture parameters depends on the generation principle and reconstruction method. Therefore, to research theories and methods of personalized human body generation and cooperative motion of human limb for speeding up the development of virtual reality and3D garment CAD technology has important theoretical significance and application value.
3D personalized human body generation and cooperative motion of human limb include the theories and methods of the establishment of human models database, generation of personalized human body, extraction of human fringe skeleton and3D reconstruction of human poses. Our overall aim is to construct the platform of Internet-oriented customization and display garments, and research related algorithms on the basis of it.
The generation method of different human shapes based on3D entity's multi-direction cooperative deformation is proposed. The used template and target human models are firstly selected in the existed database of human models, and then the models are segmented into3D entities by using figure design modeling software. Various parameters are demonstrated by using the common properties of various entities. Shape characteristics of template entity and variation range of target entity are then extracted. Subsequently, direction curves of entities are generated using proposed entity curve interpolation algorithm. Various entities are obtained by the proposed method of minimum distance criterion, and accurate different human shapes are then generated.
The generation method of personalized human model based on3D entities and multi-direction human contours is proposed. The principle of pixel size on human body image using the method of reference is firstly demonstrated. Based on the multi-direction human body contours, entity direction measurements are extracted by the proposed method combining part partitioning with human skeleton segment. The similar human entities are determined by searching for the human entity database using the graded matching based on height and entity direction measurements, and they can provide a basis for the deformation of human models. The3D human models with the shape characteristics of target model are generated by the proposed deformation method based on multi-direction human body contours using the located critical feature points and contours.
The extraction method of human fringe skeleton based on the improved Hopfield neural network and direction features is proposed. The torso of human model is firstly located using the depth direction feature of model's triangle meshes based on the definition of coronal plane. The feature points of torso on target human body are located using the feature points'location method combining the improved Hopfield neural network with direction feature. Subsequently, the feature points of limb are extracted accurately by the proposed limb direction feature using the algorithm of inclined section lines. Finally, the accurate fringe skeleton of target human body is extracted precisely based on human skeleton model.
The cooperative motion method of human limb based on conformal transformation is proposed. The joint feature points of target human body are located by the proposed method combining color histogram with distance classification. The3D coordinates of skeleton points are obtained by the method of iterative calculating scale factor based on interconnected skeleton segment using the model of human skeleton points and segments. Then, the cooperative motion method of human limb based on conformal transformation is proposed based on calculation of direction and perspective of limb motion. Therefore, the limb cooperative motion and3D human posture reconstruction can be obtained by the above methods.
The two modules of the generation of3D personalized human models and cooperative motion of human limb have been developed. Using the above modules, a lot of examples including the generation of entity direction curve, the extraction of human body contours based on human body images, the generation of3D personalized human model, the extraction of3D human fringe skeleton and the reconstruction of3D human posture are given. Furthermore, it also proves the correctness and efficiency of the proposed methods.
三维个性化人体生成和肢体协同运动包括人体模型数据库的建立、个性化人体生成、线状骨架提取和人体姿态的三维重构理论与方法,整体目标在于构建面向互联网的用户服装个性化定制和展示平台,并在此基础上实现相关算法的研究。
提出基于三维图元的多方向协同变形的不同体型生成方法。首先通过现有模型库选择所使用的模板人体模型和目标人体模型,随后利用人体造型设计软件对模型进行了三维图元划分；利用不同图元的共性,抽象得到三维图元的各种参数,提取获得了模板图元的形状特征和目标图元的变化范围,提出图元曲线插值算法以生成图元方向截线；进而提出了最小距离准则的方法得到人体的不同图元,从而获得准确的不同人体体型。
提出基于三维图元和多方向轮廓线的个性化人体生成方法。阐述了参照法提取图像像素尺寸的原理和方法；根据不同方向的人体图像轮廓线；将部分分块和人体骨架相结合,提取图元方向尺寸；通过身高和图元方向尺寸分级匹配对人体图元数据库进行搜索,以确定相似的人体图元,从而为模型的变形提供依据；利用定位的人体轮廓线和关键特征点,提出了多方向轮廓线驱动变形的方法,生成了具有目标体型特征的三维人体模型。
提出基于改进Hopfield神经网络和方向特征的人体线状骨架提取方法。根据人体模型冠状面的定义,针对模型三角片提出了深度信息的概念,用以定位人体的躯干部位；提出了改进Hopfield神经网络和方向特征相结合的特征点定位方法,实现目标人体的躯干特征点定位；介绍了模型的斜截线原理,并提出利用肢体方向特征获取准确的人体肢体特征点的方法；基于人体的骨架模型,给出了准确的目标人体线状骨架。
提出基于共形变换的人体肢体的协同运动方法。提出了颜色直方图和距离分类相结合的方法,实现了对目标人体关节特征点的定位；阐述了人体骨架点和骨架段模型,并通过相互连接骨架点迭代计算尺度因子的方法,得到了骨架点的三维信息；在对运动方向与角度进行计算的基础上,提出了基于共形变换的人体肢体的协同运动方法,从而实现了人体肢体的协同运动以及人体姿态的三维重构。
最后,对三维个性化人体生成和肢体的协同运动两个模块进行了实现,并给出了图元方向截线的生成、基于图像的人体轮廓线提取、三维个性化人体生成、三维人体线状骨架提取和重构三维人体姿态等的实例,进一步验证了所提出的方法的正确性和有效性。
In recent years, the generation of3D personalized human body and the cooperative motion of human limb have become the important development direction for the Virtual Reality field. As a core part of3D virtual garment CAD system, it allows user to select and modify the styles and sizes of garments, and presents to the user by using the method of3D display. In addition,3D reconstruction of different human poses will lay a solid foundation for dynamic display and comfort evaluation on garments. If the3D human models obtained by the method of personalized generation and cooperative motion of limb can accurately reflect shape indicators and posture parameters depends on the generation principle and reconstruction method. Therefore, to research theories and methods of personalized human body generation and cooperative motion of human limb for speeding up the development of virtual reality and3D garment CAD technology has important theoretical significance and application value.
3D personalized human body generation and cooperative motion of human limb include the theories and methods of the establishment of human models database, generation of personalized human body, extraction of human fringe skeleton and3D reconstruction of human poses. Our overall aim is to construct the platform of Internet-oriented customization and display garments, and research related algorithms on the basis of it.
The generation method of different human shapes based on3D entity's multi-direction cooperative deformation is proposed. The used template and target human models are firstly selected in the existed database of human models, and then the models are segmented into3D entities by using figure design modeling software. Various parameters are demonstrated by using the common properties of various entities. Shape characteristics of template entity and variation range of target entity are then extracted. Subsequently, direction curves of entities are generated using proposed entity curve interpolation algorithm. Various entities are obtained by the proposed method of minimum distance criterion, and accurate different human shapes are then generated.
The generation method of personalized human model based on3D entities and multi-direction human contours is proposed. The principle of pixel size on human body image using the method of reference is firstly demonstrated. Based on the multi-direction human body contours, entity direction measurements are extracted by the proposed method combining part partitioning with human skeleton segment. The similar human entities are determined by searching for the human entity database using the graded matching based on height and entity direction measurements, and they can provide a basis for the deformation of human models. The3D human models with the shape characteristics of target model are generated by the proposed deformation method based on multi-direction human body contours using the located critical feature points and contours.
The extraction method of human fringe skeleton based on the improved Hopfield neural network and direction features is proposed. The torso of human model is firstly located using the depth direction feature of model's triangle meshes based on the definition of coronal plane. The feature points of torso on target human body are located using the feature points'location method combining the improved Hopfield neural network with direction feature. Subsequently, the feature points of limb are extracted accurately by the proposed limb direction feature using the algorithm of inclined section lines. Finally, the accurate fringe skeleton of target human body is extracted precisely based on human skeleton model.
The cooperative motion method of human limb based on conformal transformation is proposed. The joint feature points of target human body are located by the proposed method combining color histogram with distance classification. The3D coordinates of skeleton points are obtained by the method of iterative calculating scale factor based on interconnected skeleton segment using the model of human skeleton points and segments. Then, the cooperative motion method of human limb based on conformal transformation is proposed based on calculation of direction and perspective of limb motion. Therefore, the limb cooperative motion and3D human posture reconstruction can be obtained by the above methods.
The two modules of the generation of3D personalized human models and cooperative motion of human limb have been developed. Using the above modules, a lot of examples including the generation of entity direction curve, the extraction of human body contours based on human body images, the generation of3D personalized human model, the extraction of3D human fringe skeleton and the reconstruction of3D human posture are given. Furthermore, it also proves the correctness and efficiency of the proposed methods.
引文
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