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CS 416
Artificial Intelligence
Lecture 23
Computer Animation
No class on Wednesday
Unfortunately I will be out of town
Final Exam
The final exam…
OLS 009, December 17th at 7:00 p.m.
It will not be cumulative
Study sheet, midterm answers, and extended
office hours will be forthcoming
Halo Tournament
Halo 2
Saturday, Dec. 11th
6:30
$3 for ACM members, $5 for others
Teams of Four
Big Prize
Details: http://acm.cs.virginia.edu/halo
Fun Stuff
Why do I love this stuff?
Impact of Video Games
Annual video game revenues surpass box office
Worldwide
revenues
$6 Million Man
$7 Billion Man
$5.6 Billion Man
Number of transistors on GPU doubles each 6
mos.
• Three times Moore’s Law
But It’s Motion That Matters
But It’s Motion That Matters
Animation Techniques
Keyframing
Animation Techniques
Motion Capture
Microsoft Motion Capture Group
Motion Analysis
Michael Gleicher
Animation Techniques
Procedural
Characters Physically Simulated
• 12 Rigid Bodies
• 17 Controlled Degrees of
Freedom
• Body Segment Densities
from Biomechanical Data
• Mass and Moments of
Inertia Calculated from
Polygonal Model
Torques Applied at Joints
Rider attached to the seat
Forces applied by
rider on handlebars
Wheels roll on
ground without
slipping
Forces applied by rider
on pedals
Locomotion Control
Correct for errors in:
Speed
Hodgins, Wooten, Brogan, and O’Brien
SIGGRAPH 1996
Orientation
Roll
Physical Limitations
Action Lags
• Limited acceleration
• Maximum/Minimum velocities
Mobility Constraints
• Architecture of Actor
How Mobile Are You?
Build a Model of Human Mobility
• Use motion capture
• Use simulation
• Use biomechanics
Modeling Pathologies
What is optimal locomotion strategy in
presence of neuromuscular malfunction?
• Large search space
– Limb trajectories
– Joint torques
– Time to complete movement
Brogan, Sheth (Mechanical Eng), and Granata (Biomed)
Applied Modeling and Simulation 2002
Spacetime Constraints
Witkin and Kass, 1988
• An optimization method
– Shoot for the stars
– Deal with physics
later
Human Path Planning
Simulate goal-oriented walking in cluttered room
• Parameterize simplified human dynamical model
• Learning by example
Brogan and Johnson (Psyc undergrad)
Graphics Interface 2003 (to be submitted)
Five Experimental Conditions
Track trajectories while moving from A to B
• 40 participants, 7 paths each
Build Model
• Speed Profiles
– acceleration and deceleration fit to cubic (distance to
goal)
– Min/max speed
– slow while turning
• Turning radius
• Subgoals located at closest points to obstacles
• Compromise between current and direct trajectories
– Inertial dampening
Evaluation
• Compare to two A* models with multiple lookahead values
• Evaluate
– piecewise errors in position / velocity
– area between paths
Image-based Method
Image matching is efficient
• Video Textures – Schodl et al.,
SIGGRAPH 2000
Image matching must be carefully normalized
• Cross-correlation between current image and all in
database
• Assign a matching weight to each image in database
Simulation Level of Detail
Planning actions of
bicyclists
• Model must include
plan for where bicyclist
would like to go
• Modified by knowledge
of what bicyclist can
accomplish
Brogan and Hodgins
AAMAS 2002
Know Your Limits
Heuristically Tuned
Previous results – obstacle avoidance
Selecting an Action
Use Simplified Models to Predict
Building Simplified Simulations
Explore range of actions in many
circumstances
Store results in compact representation
Results
Bicyclist
Average
Tracking Error
Results
Sum Position
Errors
Simulate
120 Sec
We discuss good papers like these
in the animation course
• Neuroanimator (Grzeszczuk et al.)
• video
• Through the lens camera control (Gleicher and
Witkin)
• Video Textures (Schodl et al.)
• video
• Flocks, Herds, and Schools (Reynolds)
• Escape panic / Pedesetrian Crowds (Helbing)
Automatic Motion Capture Estimation (O’Brien et al.)