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Motion Graph for Crowd

Motion Graph for Crowd. Tao Yu. Problem Description. Given a set of characters and a set of constraints. The constraints could be: Character pose. Position (p) and Orientation ( θ ). Time interval [t a , t b ] in which the configuratoin should be obtained (possibly t a =t b ).

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Motion Graph for Crowd

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  1. Motion Graph for Crowd Tao Yu

  2. Problem Description • Given a set of characters and a set of constraints. • The constraints could be: • Character pose. • Position (p) and Orientation (θ). • Time interval [ta, tb] in which the configuratoin should be obtained (possibly ta=tb).

  3. Problem Description Goal: Synthesize motion which: • Realistic • Satisfying all constraints • Collision free (already solved by navigation algorithm?)

  4. Motion Graph • Each node contains a specific pose. • Each edge corresponds to a motion clip. • Any sequence of connected edges yields a seamless motion

  5. Motion Graph • Traditional MG based methods are limited in that: • The constraints on continuous properties (position, orientation and duration) are hard to exactly satisfy. • Search is expensive.

  6. Solution (M.Sung, L.Kovar & M. Gleicher) The basic idea is: • Search in Motion Graph for motions satisfying constraints approximately • Refine rough motions thru a randomized search algorithm so that it exactly conforms to constraints

  7. Process Overview • Construct Motion Graph [GSKJ03, Snap-together motion] • Sequential processing for each character • Using PRM as path planner to create constraints sequence (way point sequence) • Search for seed motions that satisfying constraints roughly • Adjusting and merging seed motions

  8. The following contents are copied from authors’ presentation slides

  9. Rough planning PRM query Fine planning Greedy search Create seed motions If distance > ε Randomly select and replace a clip Joining with adjustment Example Algorithm Target Obstacle Initial waypoints

  10. Rough planning PRM query Fine planning Greedy search Create seed motions If distance > ε Randomly select and replace a clip Joining with adjustment Example Algorithm Target Obstacle Initial 1 2 3 waypoints

  11. Rough planning PRM query Fine planning Greedy search Create seed motions If distance > ε Randomly select and replace a clip Joining with adjustment Example Algorithm Target Obstacle Forward Motion(Mf) Initial 1 Backward Motion(Mb) 2 3 Initial’

  12. Rough planning PRM query Fine planning Greedy search Create seed motions If distance > ε Randomly select and replace a clip Joining with adjustment Algorithm Cost function : How close are they? C(Mf, Mb) > ε Forward motions Backward motions Compare all pair of motions and returns minimum cost

  13. Rough planning PRM query Fine planning Greedy search Create seed motions If distance > ε Randomly select and replace a clip Joining with adjustment Algorithm < ε New motions Old Motions Old Motionsc Random select and Replace a clip

  14. Rough planning PRM query Fine planning Greedy search Create seed paths If distance > ε Randomly select and replace a clip Joining with adjustment Example Algorithm Target Obstacle Initial Joining waypoints

  15. Motion adjustment Old Motions New motions New motions Old Motions ε The error is distributed to the both paths

  16. Comments • This method combines path planning, collision avoidance and motion synthesis together. • Suitable for high-level behavior planner. • Not directly applicable to our existing navigation/path planning methods for crowd.

  17. Potential adaptation • Dense constraints • Motion prediction (Search motion in advance) • To be added…

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