【摘要】 运动相似性度量是基于运动图的人体运动合成技术的关键.现有方法主要使用运动捕捉数据姿态数值特征进行相似性度量与合成控制,很难处理语义描述上同类型运动数据集中的不同运动实现版本间的时间与空间特征分布变化的问题.提出了引入用户语义控制来改进基于运动图的人体运动合成方法.使用关系特征作为高层语义描述,刻画同类型运动的空间变化特征;通过自学习过程,获得运动类模板,作为同类型运动的空-时特征语义描述;通过将运动类模板数据与人体运动序列文件进行匹配,实现运动类型识别和自动语义信息标注;借助关系特征语义描述及运动序列文件的语义标注信息,实现在基于运动图的运动合成中引入用户直观的高层语义控制.运动合成实验结果显示了该方法的有效性,为获得高质量人体运动合成数据提供途径.更多还原

【Abstract】 A suitable motion similarity measurement plays an important role in motion analysis for motion graph based human motion synthesis.Most existing methods of motion synthesis are based only on numerical similarity measure of motions,which cannot identify the semantically similar motions in the presence of significant spatial and temporal variations.This paper focuses on introducing semantic control into motion graph based motion synthesis.Relational features are implemented for describing and specifying motions at a high semantic level to cope with spatial variations within a class of semantically similar motions.Motion templates are then automatically derived based on a self-learning procedure from the training motions for capturing the spatio-temporal characteristics of an entire given class of semantically related motions.Automatic semantic annotation is performed on the unknown motion data document by identifying the presence of certain motion class from matching their respective motion class templates.Finally,the semantic control is introduced into motion graph based human motion synthesis,which provides user the higher level of intuitive semantically controls on motion synthesis.Experiments of motion synthesis demonstrate the effectiveness of the approach which achieves high quality output of human motion synthesis from motion capture database.更多还原