automated extraction and parameterization of motions in large data sets
DESCRIPTION
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 - PowerPoint PPT PresentationTRANSCRIPT
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Automated Extraction and Parameterization of
Motions in Large Data Sets
SIGGRAPH’ 2004
Lucas Kovar, Michael Gleicher
University of Wisconsin-Madison
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CAIG/CS/NCTU 2
Outline
Introduction
Searching for Motions
Parameterizing Motion
Results & Discussion
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CAIG/CS/NCTU 3
Introduction
GoalFinding similar motion segments in a data set and using them to construct parameterized motions
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CAIG/CS/NCTU 4
Introduction (Cont.)
HowSearching “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 mo
tion parameters, actually we want F¯¹
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CAIG/CS/NCTU 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
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CAIG/CS/NCTU 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
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CAIG/CS/NCTU 7
Searching for Motions (Cont.)
D(F1, F2) : distance between two frames of motion( Kovar SCA 2003)
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CAIG/CS/NCTU 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
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CAIG/CS/NCTU 9
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CAIG/CS/NCTU 10
Searching With Match WebsMatch sequence
Remove whose avg cell value if greater than € and remove redundant
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CAIG/CS/NCTU 11
Searching With Match Webs
Match graph
Node: motion segments
Edge: time alignment
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CAIG/CS/NCTU 12
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
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CAIG/CS/NCTU 13
Parameterizing Motion (Cont.)
Motion registration
Sampling strategy
Fast interpolation that preserves constraints
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CAIG/CS/NCTU 14
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
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CAIG/CS/NCTU 15
SamplingProduce a dense sampling of parameter space to fill the gaps
Compute the parameters of each example motionCompute a bounding boxRandomly sample points in this region
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CAIG/CS/NCTU 16
Interpolation
Given a new set of parameters , to find blend weights
D(): distance between two parameters
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CAIG/CS/NCTU 17
Interpolation (Cont.)
Parameters that are not attainable are projected onto the accessible region of parameter space
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CAIG/CS/NCTU 18
Results and Discussion
Future worksThe 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