An automatic 3D face model segmentation for acquiring weight motion area

Rio Caesar, Suyoto, Samuel Gandang Gunanto
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Abstract

Inside facial animation works there is an animator that need to be skilled enough to produce detailed animation, so the facial animation can be smooth when doing facial expressions. Every animated character requires special handling based on the characteristics of the size and location of the bone. This process, where every face model need special handling were time consuming and tedious work. For that issue this research propose method for using motion capture marker data in 3D face model for automatically segment weight motion area based on the feature point. Marker data that came from motion capture of human model will be used to represent a centroid of vertex cluster that forming expressions in animated character. The data grouping process will be spherical coordinate result calculation between feature point and vertices using modified nearest neighbor algorithm. The result obtained in this research will show the weight motion area that generated automatically from the feature point based on nearest neighbor algorithm in a 3D face model.
一种用于获取权重运动区域的三维人脸模型自动分割方法
在面部动画作品中,有一个动画师需要足够的技能来制作详细的动画,所以面部动画在做面部表情时可以流畅。每个动画角色都需要根据骨骼的大小和位置进行特殊处理。在这个过程中,每个人脸模型都需要特殊处理,这是一项耗时且繁琐的工作。针对这一问题,本研究提出了在三维人脸模型中利用运动捕捉标记数据,基于特征点自动分割权重运动区域的方法。来源于人体模型动作捕捉的标记数据将被用来表示动画角色中形成表情的顶点簇的质心。数据分组过程是利用改进的最近邻算法计算特征点与顶点之间的球坐标结果。本研究的结果将显示三维人脸模型中基于最近邻算法的特征点自动生成的权重运动区域。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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