Automated Extraction and Parameterization of Motions in Large Data Sets
ACM Transcations on Graphics, Volume 23, Number 3, page 559--568 — aug 2004
Large motion data sets often contain many variants of the same kind
of motion, but without appropriate tools it is difficult to fully exploit
this fact. This paper provides automated methods for identifying
logically similar motions in a data set and using them to build
a continuous and intuitively parameterized space of motions. To
find logically similar motions that are numerically dissimilar, our
search method employs a novel distance metric to finnd "close" motions
and then uses them as intermediaries to find more distant motions.
Search queries are answered at interactive speeds through a
precomputation that compactly represents all possibly similar motion
segments. Once a set of related motions has been extracted,
we automatically register them and apply blending techniques to
create a continuous space of motions. Given a function that de-
fines relevant motion parameters, we present a method for extracting
motions from this space that accurately possess new parameters
requested by the user. Our algorithm extends previous work by explicitly
constraining blend weights to reasonable values and having
a run-time cost that is nearly independent of the number of example
motions. We present experimental results on a test data set of
37,000 frames, or about ten minutes of motion sampled at 60 Hz.
Images and movies
BibTex references
@Article{KG04, author = "Kovar, Lucas and Gleicher, Michael", title = "Automated Extraction and Parameterization of Motions in Large Data Sets", journal = "ACM Transcations on Graphics", number = "3", volume = "23", pages = "559--568", month = "aug", year = "2004", url = "http://graphics.cs.wisc.edu/Papers/2004/KG04" }