摘要
为了解决参数化运动合成方法中普遍存在的参数结构不统一和可理解性差的问题,提出一种稀疏语义参数化模型.该模型对预处理后的运动样本进行时序成组的稀疏主成分分析,得到若干个稀疏基向量;将运动参数表示为原始变量的稀疏线性组合,而组合系数对应着稀疏基向量的分量,因此运动参数具有鲜明语义.实验结果表明,采用文中模型只需简单地修改运动参数的数值,就能够实时地控制如摆臂幅度、肘部弯曲程度、行走路径、步幅、跳跃距离等运动属性,从而直观、高效地合成出符合用户要求的逼真运动.
Since the parameters of existing parametric motion synthesis approaches are structure- inconsistent and have less intuitive meanings, we propose a sparse semantic parametric model. Our method extracts several sparse bases vectors based on Sparse Principal Component Analysis and Group Lasso, so that the original motion is represented as the linear combination of these bases and their weights in the combination corresponds to the motion parameters. Our experiments demonstrate the extracted bases have intuitive meanings, and each motion parameter controls motions in a specific aspect. Taking the walking motion as an example, our method can easily adjust stride, swinging ranges of arms, etc. by modifying the parameters.
出处
《计算机辅助设计与图形学学报》
EI
CSCD
北大核心
2013年第3期341-349,共9页
Journal of Computer-Aided Design & Computer Graphics
基金
江苏省2011年度普通高校研究生科研创新计划项目(CXLX11_0260)
南京理工大学自主科研专项计划资助项目(2011YBXM79)
