1. A Physically-Based Motion Retargeting Filter SEYOON TAK HYEONG-SEOK KO ACM TOG (January 2005) 9557526 方奎力

2. Outline Introduction Approach Result Conclusion

3. Introduction Constraints-based motion edit Kinematically constrains Dynamic constrains Segment weights 、 joint strengths …

4. Introduction Novel constraints-based motion edit Per-frame algo. -> Kalman filter May velocity relationship between constrains -> least-squares filter

5. Approach Formulation Constraints Kalman Filter Least-Squares Filter

6. Approach I. (Formulating constraints) Kinematics Balance Torque limit Momentum

7. Approach I. (Formulating constraints) Kinematics Locations e

8. Approach I. (Formulating constraints) Balance Human are two-legged creatures -> balance

9. Approach I. (Formulating constraints) Balance

10. Approach I. (Formulating constraints) Torque limit

11. Approach I. (Formulating constraints) Momentum Linear momentum Angular momentum

12. Approach II. (Kalman filter) Kalman filter

13. Approach II. (Kalman filter) Unscented Kalman filter (UKF) Better handle severe nonlinearity

14. Approach II. (Kalman filter) Unscented Kalman filter (UKF) Process model Measurement Measurement model

15. Approach II. (Kalman filter) Unscented Kalman filter (UKF) 1. Vx : process noise covariance

16. Approach II. (Kalman filter) Unscented Kalman filter (UKF) 2. Construct (2n+1) sample point

17. Approach II. (Kalman filter) Unscented Kalman filter (UKF) 3. Transform sample point through measurement model

18. Approach II. (Kalman filter) Unscented Kalman filter (UKF) 4. Predicted measurement innovation covariance cross-covariance measurement noise covariance

19. Approach II. (Kalman filter) Unscented Kalman filter (UKF) 5. Final state update

20. Approach III. (Least squares filter) Independent variables Curve fitting procedure

21. Approach III. (Least squares filter) Formulate B-spline curve

22. Approach III. (Least squares filter) Over-constrained linear system

23. Result

24. Conclusion Adv. Per-frame algo -> Stable interactive rate Constraints-base Balance constrains

25. Conclusion Disadv. Noise covariance Cost of least square filter Balance constrains -> You can ’ t fall

26. Q & A