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A Physically-Based Motion Retargeting Filter

A Physically-Based Motion Retargeting Filter. SEYOON TAK HYEONG-SEOK KO ACM TOG (January 2005) 9557526 方奎力. Outline. Introduction Approach Result Conclusion. Introduction. Constraints-based motion edit Kinematically constrains Dynamic constrains Segment weights 、 joint strengths ….

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A Physically-Based Motion Retargeting Filter

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  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) • Vx : process noise covariance

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

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

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

  19. Approach II. (Kalman filter) • Unscented Kalman filter (UKF) • 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

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