Geostatistical motion interpolation
Geostatistical motion interpolation
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DOI：
10.1145/1186822.1073313
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发表时间：
2005-07
期刊：
影响因子：
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通讯作者：
Tomohiko Mukai;Shigeru Kuriyama
中科院分区：
文献类型：
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作者：
Tomohiko Mukai;Shigeru Kuriyama
逼真的人类动画的常见运动插值技术是将相似的运动样本与加权函数混合，加权函数的参数嵌入在抽象空间中。然而，现有方法对统计特性不敏感，例如运动之间的相关性。此外，他们缺乏定量评估合成运动可靠性的能力。本文提出了一种将运动插值视为任意可定义参数空间中丢失数据的统计预测的方法。然后引入一种称为通用克里金法的实用地质统计学技术，用于统计估计参数空间中运动差异与距离之间的相关性。我们的方法统计优化每帧给定参数的插值内核，使用姿势距离度量来有效分析相关性。针对参数空间中表示的空间约束准确地预测运动，因此它们很少有不良伪影（如果有的话）。此属性缓解了与许多现有方法相关的空间不一致问题，例如脚滑动。此外，对预测可靠性的数值估计使得能够对运动进行自适应采样。由于插值内核是使用线性系统实时计算的，因此可以使用各种空间控件交互式地编辑运动。
A common motion interpolation technique for realistic human animation is to blend similar motion samples with weighting functions whose parameters are embedded in an abstract space. Existing methods, however, are insensitive to statistical properties, such as correlations between motions. In addition, they lack the capability to quantitatively evaluate the reliability of synthesized motions. This paper proposes a method that treats motion interpolations as statistical predictions of missing data in an arbitrarily definable parametric space. A practical technique of geostatistics, called universal kriging, is then introduced for statistically estimating the correlations between the dissimilarity of motions and the distance in the parametric space. Our method statistically optimizes interpolation kernels for given parameters at each frame, using a pose distance metric to efficiently analyze the correlation. Motions are accurately predicted for the spatial constraints represented in the parametric space, and they therefore have few undesirable artifacts, if any. This property alleviates the problem of spatial inconsistencies, such as foot-sliding, that are associated with many existing methods. Moreover, numerical estimates for the reliability of predictions enable motions to be adaptively sampled. Since the interpolation kernels are computed with a linear system in real-time, motions can be interactively edited using various spatial controls.