Precomputing Avatar Behavior From Human Motion Data

1. Precomputing Avatar Behavior From Human Motion Data Jehee Lee and Kang Hoon Lee Symposium on Computer Animation’ 04 Date: 12/5/2006 Reporter: 彭任右

2. Outline • Introduction • State-Action Model • Precomputing • Synthesis • Experiments • Discussion Precomputing Avatar Behavior From Human Motion Data

3. Goal • Pre-computation • Interactive Control • Motion Data • Minimum Runtime Cost Precomputing Avatar Behavior From Human Motion Data

4. Introduction • Preprocessing • Motion capture data • Lookup table • Input • Avatar State • Target State • Output • A sequence of action to the goal Precomputing Avatar Behavior From Human Motion Data

5. State-Action Model Precomputing Avatar Behavior From Human Motion Data

6. State-Action Model • Avatar State • S • Action • A • Target State • E • State-action Pair • {(S,E), A} Precomputing Avatar Behavior From Human Motion Data

7. State Space Precomputing Avatar Behavior From Human Motion Data

8. Precomputing • Pre-compute which action to take at any situation • Reinforcement learning • Trial-and-error • Discover which actions tend to increase the long-run sum of rewards in future trials Precomputing Avatar Behavior From Human Motion Data

9. Reward • Specific to a behavior • Approach the target • Throw punches at the target • Dynamic Programming • Randomly select one state pair • Select the action that gains the highest reward in one step • Value iteration Precomputing Avatar Behavior From Human Motion Data

10. Synthesis • Optimal • Select the action with the highest value • Multiple • Select an action with maximum weighted sum • Random • Identify a small number of preferable action • Select one randomly Precomputing Avatar Behavior From Human Motion Data

11. Experiments • Pentium 4 2.4GHz • 1GB memory • Vicon 120 FPS -> 15 FPS • 8 minutes shadowboxing • 20 different combination of punches • 5 * 5 capture region Precomputing Avatar Behavior From Human Motion Data

12. Data Annotation • Contact • If ankle or toe is close to the ground and its velocity is below some threshold • Effective Hitting • The magnitude of the fist’s velocity projected onto the forearm axis is above some threshold • 788 effective hitting points Precomputing Avatar Behavior From Human Motion Data

13. Graph Construction • Transition • If poses at frame i and frame j-1 are similar • Both left foot are about to leave the ground • Strongly Connected Component • 437 states • 27072 transitions Precomputing Avatar Behavior From Human Motion Data

14. Performance • Preprocessing • Approach • 1 hour • Throw Punches • 7 hours • Run Time • 30 animated boxers sparring • 9 second to create 1000 frames with video and sound disabled Precomputing Avatar Behavior From Human Motion Data

15. Results Precomputing Avatar Behavior From Human Motion Data

16. Discussion • Find the optimal behavior • Better than on-line search algorithm • Difficult to coordinate multiple goals • Memory intensive • Apply to other human motion • Collision detection and response Precomputing Avatar Behavior From Human Motion Data