Automated Extraction and Parameterization of

Motions in Large Data Sets

SIGGRAPH’ 2004

Lucas Kovar, Michael Gleicher

University of Wisconsin-Madison

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Outline

Introduction

Searching for Motions

Parameterizing Motion

Results & Discussion

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Introduction

GoalFinding similar motion segments in a data set and using them to construct parameterized motions

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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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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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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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Searching for Motions (Cont.)

D(F1, F2) : distance between two frames of motion( Kovar SCA 2003)

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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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Searching With Match WebsMatch sequence

Remove whose avg cell value if greater than € and remove redundant

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Searching With Match Webs

Match graph

Node: motion segments

Edge: time alignment

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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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Parameterizing Motion (Cont.)

Motion registration

Sampling strategy

Fast interpolation that preserves constraints

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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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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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Interpolation

Given a new set of parameters , to find blend weights

D(): distance between two parameters

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Interpolation (Cont.)

Parameters that are not attainable are projected onto the accessible region of parameter space

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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