基于骨干长度比例之运动重定目标算法

Motion Retargeting with Geometry Scaling

为了减轻动画制作劳动强度 ,提高动画制作产能以及自动产生仿真动画 ,提出了一种结合运动捕获器(Motion Capture)技术与逆向运动学原理的用于完成关节动画中实时运动重定目标 (Motion Retargeting)的新算法 .该算法的主要概念是首先根据“原动者”与“标的者”之身材比例 ,推算出“标的者”末端效应器之定位 ,然后再利用逆向运动学之算法求得“标的者”各关节之旋转角度 .因为该算法充分地利用了捕获器所纪录的“原动者”运动信息的密集重复性 ,而且所设计之定位法则能满足原运动对末端效应器之约束 ,所以该方法能展现出与原运动十分相似的动画 ,同时不违背原设定之约束 .实验数据也展示 ,并说明了该算法之效能与优点 .

This paper proposes an improved motion retargeting algorithm based on existing captured motion data and inverse kinematics. The basic notion of proposed algorithm is to estimate the end-effector tracjectories of target character by exploiting the ratio between skeleton lengths of the original character and the target character. Since important aspects of the original motion are identified as end-effector constraints which is satisfied during the retargeting process, the retargeted motion preserves desired main features. Moreover, the retargeted motion data behaves quite naturally, since the anthopometric proportions are incorporated in the algorithm. Several empirical tests are given to demonstrate the effectiveness and quality of our algorithm.