1 / 17

Motion Graph for Crowd

This solution utilizes a Motion Graph to synthesize realistic and collision-free motion for a crowd, satisfying given constraints on character pose, position, and time interval. The process involves constructing the graph, searching for seed motions, refining motion through a randomized search algorithm, and adjusting and merging seed motions.

chayes
Download Presentation

Motion Graph for Crowd

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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…

More Related