Computer Animation and Social
Agents 2003
Ronald A. Metoyer
Jessica K. Hodgins

Need a system to model the movement of many people walking and interacting
Want to maintain control over the path each individual takes
Hard to deal with collision avoidance with many characters
Easy to use

Reynolds
• Boid Model for flocks, schools, and herds
Pedestrian Models
• Fluid flow model
• Inter-pedestrian interaction models (Helbing and
Molnar)
– Social interaction based on + and – potential fields
– Lane formation in halls, queuing, turn taking

Exploit fact that humans have to move on a
2D plane (for the most part)
Basic level of intelligence
• Reactive path following, obstacles, other pedestrians
Social Forces Model
• Reactive control utilizes potential fields
• Obstacles are repulsive
• Goals are attractive

Point mass dynamics
• Update equation is:
• Where the force f x is obtained from the potential field
• dt is the simulation steps
• m is the mass of the character
Although goal locations can be specified, it is desirable to allow a definable path for the character to follow
• People are experts in drawing a path through a scene in the absence of moving obstacles
• Can also be generated through automatic process

User draws a spline path for character to follow
The path is converted into forces by the following:
Character will attempt to follow the direction of the path, but as it gets more off track, it’ll be pulled back stronger

Intelligence model will produce correct 2D animation in terms of obstacle avoidance, but not necessarily natural looking
Alert user to potential collisions and ask how to resolve them
Navigation Primitives
• Yield, Cut-in-front, Go-around-right, Go-around-left,
No-action
• Chosen based on traffic planning research

Focus on two tasks a pedestrian performs
• Monitoring
– Observing other pedestrians in the area to determine their navigational intents
• Yielding
– Act of adjusting velocity (Magnitude or Direction) to avoid a potential collision

Use previous direction primitive choices to aid the user in future decisions
• Direction Primitive
• Feature vector that describes current scene
– Is the path around left blocked by other pedestrians or obstacles (Y or N)
– Is the path around right blocked by other pedestrians or obstacles (Y or N)
– Relative speed of the colliding pedestrian (5)
– Approach direction of the colliding pedestrian (8)
– Colliding pedestrian’s distance to collision (5)
– Pedestrian’s distance to collision (5)
– Desired travel direction (3)

Naïve Bayes Classifier
•
Five primitives are hypotheses
• Seven variables are inputs
• Potential collisions are classified into one of the 5 primitives
Advantages
•
Outperforms neural networks and machine learning algorithms in most real life cases
Disadvantages
• Limited by the fact that it can only deal with discrete data

Use motion capture
Create a directed graph of poses to get a probability matrix for transitions from one pose to another

Compared the Naïve Bayes algorithm to actual choices made by users
• Claim 72% accuracy as opposed to a random choice which would be 20% naturally
• This doesn’t mean much, because all it is really testing is their ability to train a Bayes classifier

Requires (utilizes) a lot of human intervention
There is no motion capture data of a person stopped, so it appears the person is spinning around when standing still

Apply torque to cart’s wheels
• Balance pole
• Accomplish desired location
• Accomplish desired velocity
Extra Credit
• Swing-up task

language = C gravity = 0 0 -9.80665
prefix = cartpole
# cart is a truck-sized object, 20 x 4 x 3 feet = 6x1.5x1 meters
# with car-like density of 170 kg / m^3
# therefore, truck-like mass of 1800kg = 4000 lbs

body = cart joint = slider jname = pos mass = 1530 inertia = 414.37500000 4717.50000000 4876.87500000
bodyToJoint = 0 0 0 pin = 1 0 0

# A 300 lb = 136 kg ladder that is roughly
# 15 x 1.5 x 0.5 feet = 4.6x.45x.15 meters body = ladder inboard = cart joint = pin jname = theta mass = 52.785
inertia = 0.98971875 93.17652187 93.96829687
bodyTojoint = -2.3 0 0 inbToJoint = -3.0 0 0.75
pin = 0 1 0

More complicated simulation of girl on a swing
• Hands are rigidly attached to rope
• Butt is rigidly attached to seat
• You control torques at shoulder, elbow, hips, and knee

State machine
• Swinging has discrete modes, or states
– Define when they begin and end
– Define what movements are required for each state

Very important!!!
• Each simulation has a simulation timestep, DT
• Smaller timestep required for larger forces
– Numerical imprecision of integrator
• Make sure your simulations are precise by dropping DT by an order of magnitude and confirm behavior is the same