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Handrix:
Animating the Human Hand
George ElKoura
Karan Singh
University of Toronto
Motivation
How do we get from a piece of music:
To finger motions that play the music:
Overview
• System Architecture Overview
• Guitar Algorithm
• Anatomy of the Hand
• Hand Model
• Results
System Architecture
System Architecture
System Architecture
System Architecture
System Architecture
Related work
• Anatomy
– Häger-Ross and Schieber, 2000
• Vision
– Lee and Kunii, 1995
• Graphics
– Rijpkema and Girard, 1991
• Music
– Quine, 1990
A Bit of Anatomy
• Why are fingers interdependent?
– “One to many” muscle insertion sites
– Close proximity of tendons
– Neurological constraints
• A clear anatomical understanding is still
being developed
Hand Model
• Goal:
– Map partially specified hand positions to realistic hand postures
• Approach:
– Motion Capture lots of hand positions
– Nearest Neighbors, RBFs to approximate hand-pose space
Interdependence Assumptions
•
Assumptions to reduce number of parameters:
1. Independence of the Thumb
2. Invariance of Hand Dominance
3. Independence of Adduction/Abduction
4. Irrelevance of Frequency
5. Locality: Posture of wrist and arm do not affect digit
independence
•
These assumptions reduce the problem to 12 DOFs.
© Babette Adrian
Reference: Häger-Ross and Schieber, Journal of Neuroscience 20(22), 2000
Interdependence Reconstruction
• Allow the animator/controller to partially specify a
hand posture (0, 1, … n)
Interdependence Reconstruction
• Allow the animator/controller to supply weights for
each joint indicating the importance of the supplied
joint angle (0, 1, …, n)
0 =
1
…
n =
0
Interdependence Reconstruction
• Correct the user-supplied hand posture using the
given weights and the database of physically
possible postures
Reconstruction Method
•
The database is a collection of joint angles represented by 12D vectors: I
•
Compute the weighted distance by:
•
The k-Nearest Neighbors interpolation weights are computed by:
Interdependence Results
(No Reconstruction)
Interdependence Results
(With Reconstruction)
Interdependence Results
(Comparison)
The Guitar
6 strings numbered from 1 to 6
(heavy to light)
Frets delimit strings lengths
producing audibly different notes
Frets are numbered in increasing
order from the nut (fret 0)
Tablature
• Common guitar music notation
• Tells musician what frets to hold
• Does not tell musician what fingers to use
• Simple ASCII, readily available (OLGA)
• Adaptable to specifying multi reaching goals over time
Fretting Design Objectives
1. Economy of effort
2. Read-ahead control
3. Maintain natural motion of fingers
4. Capture differences in finger dexterity and
strengths
5. Fingertips touch guitar at the correct fret
Reference: Hector Quine, Guitar Technique, Oxford University Press, 1990
Guitar Space
Cost Functions
• Assign a different cost function to each finger, wrist
• Each cost function can be a sum of specific cost functions to
separately handle dexterity, strength and so forth
• Overall cost is computed, and cheapest fingering wins
Finger Resolution Algorithm
Finger Collisions
Finger collisions are handled in a simple way
• When a collision is detected, the colliding finger is moved
backwards on the fret
Results – C-Major Scale
Results – Chromatic Scale (fails)
Results – Chromatic Scale (passes)
Results – Chord Transitions
Results – More Chords
Results – Bar Chords
Results – C-Major Scale (3 Fingers)
Results – Stairway to Heaven
Future Work
Apply to other areas:
Future Work
• Slides, bends, hammers
• Frequency of motion
• Motion Dynamics (strumming hand)
• Anatomical Hand Model
• Extension to other areas (e.g.
locomotion)
Summary
System Architecture Overview
Guitar Algorithm
Anatomy of the Hand
Hand Model
Results
For More Information Please Visit:
http://www.dgp.toronto.edu/~gelkoura/handrix/index.html
Acknowledgements
DGP Lab, University of Toronto
Chris Landreth
Dave Baas
Side Effects Software, Inc.
Alias Systems
NSERC
For more information please visit:
http://www.dgp.toronto.edu/~gelkoura/handrix/index.html