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

2. CAIG/CS/NCTU2 Outline Introduction Searching for Motions Parameterizing Motion Results & Discussion

3. CAIG/CS/NCTU3 Introduction Goal Finding similar motion segments in a data set and using them to construct parameterized motions

4. CAIG/CS/NCTU4 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. CAIG/CS/NCTU5 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. CAIG/CS/NCTU6 Searching for Motions (Cont.) Cell(i, j) : d(M1(ti), M2(tj))d(M1(ti), M2(tj)) Find the avg and compare against a user-specified threshold € 1D minima

7. CAIG/CS/NCTU7 Searching for Motions (Cont.) D(F 1, F 2 ) : distance between two frames of motion( Kovar SCA 2003)

8. CAIG/CS/NCTU8 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. CAIG/CS/NCTU9

10. 10 Searching With Match Webs Match sequence Remove whose avg cell value if greater than € and remove redundant

11. CAIG/CS/NCTU11 Searching With Match Webs Match graph Node: motion segments Edge: time alignment

12. CAIG/CS/NCTU12 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

13. CAIG/CS/NCTU13 Parameterizing Motion (Cont.) Motion registration Sampling strategy Fast interpolation that preserves constraints

14. CAIG/CS/NCTU14 Registration Timewarp curve s(u) N e example motions => each point on s is an N e -dimensional vector Automatic determination may fail for more distant motions => identify the shortest path from Mq to every other motion in the match graph

15. CAIG/CS/NCTU15 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

16. CAIG/CS/NCTU16 Interpolation Given a new set of parameters, to find blend weights D(): distance between two parameters

17. CAIG/CS/NCTU17 Interpolation (Cont.) Parameters that are not attainable are projected onto the accessible region of parameter space

18. CAIG/CS/NCTU18 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