A Physically-Based Motion Retargeting Filter
SEYOON TAKHYEONG-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…
Introduction
Novel constraints-based motion edit
Per-frame algo. -> Kalman filter May velocity relationship between
constrains -> least-squares filter
Approach
Formulation Constraints Kalman Filter Least-Squares Filter
Approach I. (Formulating constraints)
Kinematics Balance Torque limit Momentum
Approach I. (Formulating constraints)
Kinematics Locations e
Approach I. (Formulating constraints)
Balance Human are two-legged creatures -> balance
Approach I. (Formulating constraints)
Balance
Approach I. (Formulating constraints)
Torque limit
Approach I. (Formulating constraints)
Momentum Linear momentum
Angular momentum
Approach II. (Kalman filter) Kalman filter
Approach II. (Kalman filter) Unscented Kalman filter (UKF)
Better handle severe nonlinearity
Approach II. (Kalman filter) Unscented Kalman filter (UKF)
Process model
Measurement
Measurement model
Approach II. (Kalman filter) Unscented Kalman filter (UKF)
1. Vx : process noise covariance
Approach II. (Kalman filter) Unscented Kalman filter (UKF)
2. Construct (2n+1) sample point
Approach II. (Kalman filter) Unscented Kalman filter (UKF)
3. Transform sample point through measurement model
Approach II. (Kalman filter) Unscented Kalman filter (UKF)
4. Predicted measurement innovation covariance
cross-covariance
measurement noise covariance
Approach II. (Kalman filter) Unscented Kalman filter (UKF)
5. Final state update
Approach III. (Least squares filter)
Independent variables Curve fitting procedure
Approach III. (Least squares filter)
Formulate B-spline curve
Approach III. (Least squares filter)
Over-constrained linear system
Conclusion Adv.
Per-frame algo -> Stable interactive rate Constraints-base Balance constrains
Conclusion Disadv.
Noise covariance Cost of least square filter Balance constrains -> You can’t fall