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.
雑誌名
Proceedings of ACM SIGGRAPH 2005 (ACM Transactions on Graphics Vol.24 Issue 3, July 2005)
巻
24
号
3
ページ
1062 - 1070
発行年
2005-07
出版者
ACM
DOI
10.1145/1073204.1073313
権利
(C) ACM 2005. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in ACM SIGGRAPH 2005 (ACM Transactions on Graphics Vol.24 Issue 3, July 2005 Pages 1062-1070, http://dx.doi.org/10.1145/1073204.1073313.
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