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Reach Out and Touch Space (Motion Learning)

Reach Out and Touch Space (Motion Learning). Luis Goncalves, Enrico Di Bernardo, Pietro Perona California Institute of Technology. Motion is an important cue. A system for body tracking (Goncalves-Di Bernardo-Perona, ICCV ‘95). Single camera Real-time estimate of body pose 3D Model-based

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Reach Out and Touch Space (Motion Learning)

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  1. Reach Out and Touch Space(Motion Learning) Luis Goncalves, Enrico Di Bernardo, Pietro Perona California Institute of Technology

  2. Motion is an important cue

  3. A system for body tracking(Goncalves-Di Bernardo-Perona, ICCV ‘95) • Single camera • Real-time estimate of body pose • 3D Model-based • No markers required • Able to track at frame rate (30 fr/sec) • 8% max error (along the line of sight) BUT • No loose clothing • Calibration on the user • Loses track for fast movements

  4. A system for body tracking(Goncalves-Di Bernardo-Perona, ICCV ‘95) Dynamical Model Estimated arm position and velocity Camera Error vector Background Subtraction Recursive Estimator Arm silhouette generation 3D Model Calibration parameters Arm model parameters

  5. Current model: Random Walk Dynamical model equations: & ì J = J ï v í & ï J = Î S w , w Ν ( 0 , ) î v

  6. Why Models for Human Motion ? • Locomotion (biomechanics, robotics) • Brain motor control (neuroscience) • Human/Machine perception of biological motion (neuroscience, psychophysics, computer vision) • Realistic animation (computer graphics)

  7. Invariant properties of ballistic point to point movements • The path is approximately straight • The tangential velocity profile has a smooth bell shape • These properties are invariant wrt subject, execution time, load carried (Hollerbach, Viviani, Flash-Hogan, Bizzi) Far from the neuro-muscular limits and after practice: No predictive power for general motion

  8. Human figure animation in CG • Keyframing, manual editing (Perlin, animation software) • Physics-based (Hodgins et al.) • Constraint optimization (Witkin and Kass, Badler et al.) • Human motion capture(Bruderlin, Rose et al.)

  9. The proposed method • Acquire sample human motions • Label each individual motion with a high level description (e.g. goal of motion) • Learn a function that maps labels to motions

  10. The 2D Motion Capture System • 14 dots on the main body joints • single camera • UV lighting • real-time detection with sub-pixel accuracy

  11. Example: Reaching motions Start from a fixed initial pose. Reach to various locations in space.

  12. 28 * N Î m M = Â T Pictorial representation N Î l = Â L Label space: L Trajectory space:

  13. The Functional Space 1. Polynomial Basis Functions d d d = × f ( l ) 1 l l L NL 1 0 j 1 N L 2. Radial Basis Functions 1 - - - T - ( l μ ) Σ ( l μ ) 1 2 j j j f = ( l ) e j

  14. Learning the function N å = = f m f ( l ) w ( l ) ˆ k k jk j = j 0 } { å * i i 2 - min arg ( f ( l ) m ) w = k k jk { w } i , k jk * = M W Φ

  15. Experiments: Reaching motions Picking up apples in 30 different locations • NL = 2 • NT = 120 90 sample motions

  16. Experiments: Drawing motions Drawing strokes on a blackboard • NL = 8 • NT = 60 110 sample strokes

  17. How to evaluate performance? • RMS error • Perceptual evaluation by subjects • other methods ...

  18. Results: %RMS error for Reaching

  19. Results: Visual DiscriminabilityReaching using 3rd order polynomial functional space

  20. Results: %RMS error for Drawing

  21. Results: Visual DiscriminabilityDrawing using 1st order polynomial functional space

  22. Combining Reaching and Drawing Blend out trajectory discontinuities at merge points of different motions.

  23. Conclusions • The method generates realistic synthetic reaching and drawing motions • The method can generate motions from a high level description • The technique can be used for animation

  24. Future work • Obtaining and running experiments with 3D captured data • Develop a perceptually motivated metric • Experiment with other high level labels such as speed, emotional state or gender • Use models for tracking/prediction

  25. Minimum jerk trajectories in reaching movements (Flash-Hogan ‘85) Explains the experimental evidence for both straight and via point reaching motions The trajectory minimizes: = t T 2 ò x ( t ) dt = & & & C = 0 t

  26. The equilibrium point trajectory (Bizzi ‘91) The muscular system has a spring-like behavior Brain signals activate entire muscle groups The activation signals depend only on the ideal (minimum jerk) trajectory

  27. Motion is a powerful cue • From the trajectories of 12 dots attached to • the main joints in the body, a subject could • distinguish human motion from other objects motion • identify gender • identify action and mood • perceive 3D structure from 2D trajectories • (Johansson ‘73, Mather ‘94, Dittrich et al. ‘96)

  28. Labeling the data

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