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Motion Modeling for Online Locomotion Synthesis

Motion Modeling for Online Locomotion Synthesis. Taesoo Kwon and Sung Yong Shin KAIST. Outline. Motivation Related work Overview Motion analysis Motion synthesis Conclusions Future Work. Motivation. Real-time locomotion synthesis Motion rearrangement : realism

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Motion Modeling for Online Locomotion Synthesis

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  1. Motion Modeling for Online Locomotion Synthesis Taesoo Kwon and Sung Yong Shin KAIST

  2. Outline • Motivation • Related work • Overview • Motion analysis • Motion synthesis • Conclusions • Future Work

  3. Motivation • Real-time locomotion synthesis • Motion rearrangement : realism • Motion blending : efficiency and controllability • Hybrid approach • Locomotive motion generation [PSS02, PSS04] • Rhythmic motion synthesis [KPS03] • Premise: motion labeling

  4. Related Work • Motion Segmentation [Bindiganavale & Badler, 1998;Fod et al., 2002; Kim et al., 2003] • Motion Classification [Arikan et al., 2003;Kovar & Gleicher, 2004;Forbes & Fiume 2005;Mueller & Roeder 2005] • Motion Labeling for blending [Kim et al., 2003]

  5. Overview example motions motion analysis hierarchical motion transition graph motion specifications desired motion motion synthesis

  6. Motion Analysis • Issues • Motion segmentation • Motion classification • Graph construction • Biomechanical observations • [Per92,Win90]

  7. transition walk run right foot left foot Biomechanical Observations • Center of mass trajectory

  8. Motion segmentation • Criteria for motion segmentation • Simple enough for intuitive parameterization • Long enough to contain motion semantics • An important motion feature should not be split  Split at every COM peak

  9. Motion Classification • String encoding • Pros • avoid troublesome time-warping • more robust than numerical computation

  10. (a) S (b) R (c) L (d) D (e) F Motion Classification • Footstep patterns

  11. Motion Classification • String Encoding (ideal case)

  12. Motion Classification • String Encoding (ideal case) R D L

  13. Motion Classification • String Encoding (ideal case) F R F

  14. Motion Classification • String Encoding (ideal case) R D L F

  15. Motion Classification • String Encoding (ideal case)

  16. Refinement • False peak • Concatenate two motion segments • Missing peak • Divide a motion segment into two

  17. Graph Construction

  18. Graph Construction

  19. Motion Analysis Results • O(n) • 2Ghz PC (AMD 64, 2GB memory) • For 7.4 min locomotion, about 10 seconds • Movie

  20. LDR RDL LDRF … Motion Synthesis …

  21. Motion Synthesis • Motion specification • Motion parameter

  22. Motion Sythesis • How to calculate • Two half cycles in cyclic motion • Regression analysis on

  23. Motion Synthesis • Motion blending : [PSS04][KG03][ACP02] • Motion stitching : [GSKJ03] • Motion retargeting : [SLSG01][KGS02]

  24. Motion Synthesis Result • 1000+ frames per second • Movie • Path following • Online synthesis

  25. Conclusion • Motion labeling based on string encodings • Hierarchical motion transition graph

  26. Future work • Footstep-driven motions such as dancing and boxing

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