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Automated Extraction and Parameterization of Motions in Large Data Sets

Lucas Kovar, Michael Gleicher
ACM Transcations on Graphics, Volume 23, Number 3, page 559--568 — aug 2004
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    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.

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    BibTex references

      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"

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