motion icon
DESCRIPTION
Motion Icon. Feng Liu Advisor: Michael Gleicher Computer Sciences Department University of Wisconsin-Madison. Goal. Motion Icon Summarize a motion capture data into a single image Application: motion database browsing. Solution. Extract key frames Pose clustering Extract key frames - PowerPoint PPT PresentationTRANSCRIPT
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Motion Icon
Feng Liu
Advisor: Michael Gleicher
Computer Sciences Department
University of Wisconsin-Madison
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Goal Motion Icon
Summarize a motion capture data into a single image
Application: motion database browsing
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Solution Extract key frames
Pose clustering Extract key frames
Render key frames Re-position key frames Determine proper camera settings to
render them effectively
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Feature dimension reduction Decomposing motion using Singular
Value Decomposition (SVD)
Select the q most significant singular values
Reconstruct new ‘motion’ M ‘
NNNTTTNT VSUM
qqqTqT SUM ''
qqS '
Only need 8~15/57 DOFs
to keep 90-95% singular values
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Feature dimension reduction
first 3 new motion signals of M’Singular values from decomposing a walking motion using SVD
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Pose clustering Unsupervised clustering method based
on Gaussian Mixture Models Estimate a GMM model for a motion using
Expectation-Maximization (EM) Initialize the clusters using the Gaussian
Mixture components Merge 2 closest clusters greedily until only 1
cluster is left Select the number of clusters with minimal
Rissanen cost
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Rissanen cost A combination of fitting errors and the
number of clusters
)log()1)2
*)1(1((
2
1),|(log),( TN
NNNKKypKMDL y
fitting errors number of clusters
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Clustering procedure
minimal cost with 4 clusters
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Clustering examples
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Extract key frames First frames of each cluster as key
frame
Shortest path from cluster graph containing all the clusters
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First frame scheme
?
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Shortest path scheme Shortest path from Cluster Graph
Containing all the clusters
C2 C0 C1
C3
Cluster graphC2 C0 C3 C1
Shortest path
Cluster sequence
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Path-finding algorithm A variation of Hamiltonian path: NP-hard ! Greedy approximation
Construct cluster sequence Greedily shorten the cluster sequence
Find all sub-paths start and end with the same cluster, all the intermediate vertices exist in the other part
of the cluster sequence Select the shortest path, and reduce it
Eliminate redundant vertices at the beginning and the end of the path
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Path-finding algorithm
C2 C0 C1 C0 C2 C0 C3 C1 C0 C2 C0 C3
C2 C0 C2 C0 C3 C1 C0 C2 C0 C3
C2 C0 C3 C1 C0 C2 C0 C3
C2 C0 C3 C1 C0 C3
C2 C0 C3 C1
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Shortest path
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Re-position key frames Along user-specified routes
Line Circle Grid ……
Lost motion trajectory info.
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Re-position key frames Along the original motion trajectory
Scale the motion trajectory Evenly position the key frames
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Proper camera setting selection Goal
Render key frames in a way with minimal key frame occlusion
At vector the center of the root trajectory
Up vector Interpolation btw [0 1 0] and the minor motion
axis Eye vector
Eye-At line perpendicular to the plane determined by the the Up vector and the major motion axis
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Camera settings
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Results
Motion icon
Walk containing 559 frames
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Results
Motion icon
High-wire Walk containing 548 frames
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Results
Motion icon
“Walk” containing 236 frames
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Results
Motion icon
“Ballet” containing 1022 frames
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Results
Motion icon
“Faint” containing 145 frames
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More icons
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Conclusion A complete framework for creating
motion icon SVD based feature reduction GMM based unsupervised pose clustering Cluster graph based key frame extraction Key frame reposition methods Motion trajectory based camera setting
determination
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Thank You