a physically-based motion retargeting filter

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

Result

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

Q & A