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Automated Extraction and Parameterization of Motions in Large Data Sets

Automated Extraction and Parameterization of Motions in Large Data Sets. SIGGRAPH’ 2004 Lucas Kovar, Michael Gleicher University of Wisconsin-Madison. Outline. Introduction Searching for Motions Parameterizing Motion Results & Discussion. Introduction. Goal

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Automated Extraction and Parameterization of Motions in Large Data Sets

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  1. Automated Extraction and Parameterization of Motions in Large Data Sets SIGGRAPH’ 2004 Lucas Kovar, Michael Gleicher University of Wisconsin-Madison

  2. Outline • Introduction • Searching for Motions • Parameterizing Motion • Results & Discussion

  3. Introduction • Goal • Finding similar motion segments in a data set and using them to construct parameterized motions

  4. Introduction (Cont.) • How • Searching “Similar” Motion Data Sets • Multi-step search • Using time correspondences to determine similarity • Interactivity through precomputation(match web) • Creating Parameterized Motions • User-specified function F maps blend weights to motion parameters, actually we want F¯¹

  5. Searching for Motions (Cont.) • Determine similarity • Corresponding frames should have similar skeleton poses • Frame correspondences should be easy to identify • Time alignment • Monotonically increasing • Continuous • Non-degenerate

  6. Searching for Motions (Cont.) • Cell(i, j) : d(M1(ti), M2(tj)) • Find the avg and compare against a user-specified threshold € • 1D minima

  7. Searching for Motions (Cont.) • D(F1, F2) : distance between two frames of motion( Kovar SCA 2003)

  8. Match Webs • Looking for chains of 1D minima • Remove chains below a threshold length • Connecting chains as long as the connecting path is inside the valid region and has a length less than a threshold L • Valid region: extend local minima

  9. Searching With Match Webs • Match sequence • Remove whose avg cell value if greater than € and remove redundant

  10. Searching With Match Webs • Match graph • Node: motion segments • Edge: time alignment

  11. Parameterizing Motion • F: maps a set of blend weights w to a parameter vector p • What we want: a set of parameters => blend weights that produce the corresponding motion • Not guaranteed to be dense or uniform => generate blends to create additional samples

  12. Parameterizing Motion (Cont.) • Motion registration • Sampling strategy • Fast interpolation that preserves constraints

  13. Registration • Timewarp curve s(u) • Ne example motions => each point on s is an Ne-dimensional vector • Automatic determination may fail for more distant motions => identify the shortest path from Mq to every other motion in the match graph

  14. Sampling • Produce a dense sampling of parameter space to fill the gaps • Compute the parameters of each example motion • Compute a bounding box • Randomly sample points in this region

  15. Interpolation • Given a new set of parameters , to find blend weights • D(): distance between two parameters

  16. Interpolation (Cont.) • Parameters that are not attainable are projected onto the accessible region of parameter space

  17. Results and Discussion • Future works • The development of alternatives to match webs that are more efficient • Developing methods to ease the data requirements while preserving motion quality • Construct more parameterized motion, ex: leaping motion

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