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Motion Icon

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

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Motion Icon

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  1. Motion Icon Feng Liu Advisor: Michael Gleicher Computer Sciences Department University of Wisconsin-Madison

  2. Goal • Motion Icon • Summarize a motion capture data into a single image • Application: • motion database browsing

  3. Solution • Extract key frames • Pose clustering • Extract key frames • Render key frames • Re-position key frames • Determine proper camera settings to render them effectively

  4. Feature dimension reduction Only need 8~15/57 DOFs to keep 90-95% singular values • Decomposing motion using Singular Value Decomposition (SVD) • Select the q most significant singular values • Reconstruct new ‘motion’ M ‘

  5. Feature dimension reduction Singular values from decomposing a walking motion using SVD first 3 new motion signals of M’

  6. 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

  7. Rissanen cost • A combination of fitting errors and the number of clusters fitting errors number of clusters

  8. Clustering procedure minimal cost with 4 clusters

  9. Clustering examples

  10. Extract key frames • First frames of each cluster as key frame • Shortest path from cluster graph • containing all the clusters

  11. First frame scheme

  12. Cluster sequence Shortest path Cluster graph Shortest path scheme • Shortest path from Cluster Graph • Containing all the clusters

  13. 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

  14. Path-finding algorithm

  15. Shortest path

  16. Re-position key frames • Along user-specified routes • Line • Circle • Grid • …… • Lost motion trajectory info.

  17. Re-position key frames • Along the original motion trajectory • Scale the motion trajectory • Evenly position the key frames

  18. 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

  19. Camera settings

  20. Results Motion icon Walk containing 559 frames

  21. Results Motion icon High-wire Walk containing 548 frames

  22. Results Motion icon “Walk” containing 236 frames

  23. Results Motion icon “Ballet” containing 1022 frames

  24. Results Motion icon “Faint” containing 145 frames

  25. More icons

  26. 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

  27. Thank You

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