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Segmenting Motion Capture Data into Distinct Behaviors
Graphics Interface ‘04
Speaker: Alvin
January 17, 2005
Alvin/GAME Lab./CSIE/NDHU
Segmenting Motion Capture Data into Distinct Behaviors
2
Outline
• Introduction
• Related Work
• PCA
• PPCA
• GMM
• Results
• Conclusions
Alvin/GAME Lab./CSIE/NDHU
Segmenting Motion Capture Data into Distinct Behaviors
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Introduction
• Motion data are segmented at capture or by hand and are often small clips.
• Longer shots contain natural transitions.
• Segment motion into high-level behaviors.
• Unsupervised Learning
• Focus on efficient techniques: PCA, PPCA and GMM.
Alvin/GAME Lab./CSIE/NDHU
Segmenting Motion Capture Data into Distinct Behaviors
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Related Work
• Model-based Approach• Low-level
• Detect zero crossings of angular velocities.• Motion texton• State Machine or Motion Graph
• High-level• HMM• Clustering
Alvin/GAME Lab./CSIE/NDHU
Segmenting Motion Capture Data into Distinct Behaviors
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Goal
• Input: Motion data (14 motions, each 8000 frames)• FPS=120• 14 Joints• Specify the rotation relative to the parent for all joints.• Rotations are specified by quaternions.
• Output: Motion Clips• Automatically• Distinct Behaviors• Longer
Alvin/GAME Lab./CSIE/NDHU
Segmenting Motion Capture Data into Distinct Behaviors
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Center of motion:
Approximation:
SVD:
Dimension:
Projection Error:
Derivative:
PCA
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Alvin/GAME Lab./CSIE/NDHU
Segmenting Motion Capture Data into Distinct Behaviors
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PCA Cut if di more than 3 standard deviations from the average
Alvin/GAME Lab./CSIE/NDHU
Segmenting Motion Capture Data into Distinct Behaviors
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Probabilistic PCA
• Average square of discard singular values:
• Covariance Matrix:• Average Mahalanobis
Distance• T=150, K=T• K:=K+ , =10, Thr△ △
eshold R=15
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Alvin/GAME Lab./CSIE/NDHU
Segmenting Motion Capture Data into Distinct Behaviors
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PPCA
Alvin/GAME Lab./CSIE/NDHU
Segmenting Motion Capture Data into Distinct Behaviors
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Gaussian Mixture Model
• Pre-processing:• Use PCA to project onto lower dimensional sub
space. (Speed up EM)• Preserve 90% of the variance.• Each cluster is represented by a Gaussian Distri
bution.
• EM• Estimate mean, covariance matrix, prior
Alvin/GAME Lab./CSIE/NDHU
Segmenting Motion Capture Data into Distinct Behaviors
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GMM
Alvin/GAME Lab./CSIE/NDHU
Segmenting Motion Capture Data into Distinct Behaviors
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GMM Cut if frames xi and xi+1 belong to different clusters
Alvin/GAME Lab./CSIE/NDHU
Segmenting Motion Capture Data into Distinct Behaviors
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Results
Error Matrix for PCA Error Matrix for PPCA
Alvin/GAME Lab./CSIE/NDHU
Segmenting Motion Capture Data into Distinct Behaviors
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Results
Alvin/GAME Lab./CSIE/NDHU
Segmenting Motion Capture Data into Distinct Behaviors
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Results
Precision: Reported correct cuts / The total number of reported cuts
Recall : Reported correct cuts / The total number of correct cuts
Alvin/GAME Lab./CSIE/NDHU
Segmenting Motion Capture Data into Distinct Behaviors
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Evaluation Form• 論文簡報部份
• 完整性介紹 (4)• 系統性介紹 (4)• 表達能力 (3)• 投影片製作 (3)
• 論文審閱部分• 瞭解論文內容 (4)• 結果正確性與完整性 (4)• 原創性與重要性 (4)• 讀後啟發與應用:
The mahalanobis distance can be adopted to my classification of motions. Besides, maybe I can exploit the GMM technique to classify for comparison.
Alvin/GAME Lab./CSIE/NDHU
Segmenting Motion Capture Data into Distinct Behaviors
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Conclusions
• Imperfect because observations’ opinions.
• Treat all weights of DOF equally.
• Each method require some parameters.
• PCA-based methods work well.
• ICA may achieve better cut detection.
• No segmentation will apply for all applications.
Alvin/GAME Lab./CSIE/NDHU
Segmenting Motion Capture Data into Distinct Behaviors
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Mahalanobis Distance• Dt(x) = (x – mt)S-1
t(x – mt)'• Dt is the distance from t group
• St represents the within-group covariance matrix
• mt is the vector of the means of t group
• X is the vector of frame values at location x
• Superior to Euclidean distance because it takes distribution of the points (correlations) into account
• Useful to determine the ”similarity” from an unknown sample to known samples
• Classify observations into different groups
Alvin/GAME Lab./CSIE/NDHU
Segmenting Motion Capture Data into Distinct Behaviors
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GMM by Using EM