Motion Reconstruction Using Sparse Accelerometer Data

Jochen Tautges, Arno Zinke, Björn Krüger, Jan Baumann, Andreas Weber, Thomas Helten, Meinard Müller, Hans-Peter Seidel und Bernd Eberhardt
In: ACM Trans. Graph. (Mai 2011), 30:3(18:1-18:12)
 

Abstract

The development of methods and tools for the generation of visually appealing motion sequences using prerecorded motion capture data has become an important research area in computer animation. In particular, data-driven approaches have been used for reconstructing high-dimensional motion sequences from low-dimensional control signals. In this article, we contribute to this strand of research by introducing a novel framework for generating full-body animations controlled by only four 3D accelerometers that are attached to the extremities of a human actor. Our approach relies on a knowledge base that consists of a large number of motion clips obtained from marker-based motion capturing. Based on the sparse accelerometer input a cross-domain retrieval procedure is applied to build up a lazy neighborhood graph in an online fashion. This graph structure points to suitable motion fragments in the knowledge base, which are then used in the reconstruction step. Supported by a kd-tree index structure, our procedure scales to even large datasets consisting of millions of frames. Our combined approach allows for reconstructing visually plausible continuous motion stream even in the presence of moderate tempo variations which may not be directly reflected by the given knowledge base.

Stichwörter: acceleration data, Motion capture, motion reconstruction, motion retrieval, online control

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Bibtex

@ARTICLE{tautges-2011-MotionReconstruction,
    author = {Tautges, Jochen and Zinke, Arno and Kr{\"u}ger, Bj{\"o}rn and Baumann, Jan and Weber, Andreas and Helten,
              Thomas and M{\"u}ller, Meinard and Seidel, Hans-Peter and Eberhardt, Bernd},
     pages = {18:1--18:12},
     title = {Motion Reconstruction Using Sparse Accelerometer Data},
   journal = {ACM Trans. Graph.},
    volume = {30},
    number = {3},
      year = {2011},
     month = may,
  keywords = {acceleration data, Motion capture, motion reconstruction, motion retrieval, online control},
  abstract = {The development of methods and tools for the generation of visually appealing motion sequences using
              prerecorded motion capture data has become an important research area in computer animation. In
              particular, data-driven approaches have been used for reconstructing high-dimensional motion
              sequences from low-dimensional control signals. In this article, we contribute to this strand of
              research by introducing a novel framework for generating full-body animations
              controlled by only four 3D accelerometers  that are attached to the extremities of a human actor.
              Our approach relies on a knowledge base that consists of a large number of motion clips obtained
              from marker-based motion capturing. Based on the sparse accelerometer input a cross-domain retrieval
              procedure is applied to build up a lazy neighborhood graph in an online fashion. This graph
              structure points to suitable motion fragments in the knowledge base, which are then used in the
              reconstruction step. Supported by a kd-tree index structure, our procedure scales to even large
              datasets consisting of millions of frames. Our combined approach allows for reconstructing visually
              plausible continuous motion stream even in the presence of moderate tempo variations which may not
              be directly reflected by the given knowledge base.},
      issn = {0730-0301},
       url = {http://doi.acm.org/10.1145//1966394.1966397},
       doi = {10.1145//1966394.1966397},
articleno = 18
}