Human Simulation

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Human Simulation

• Keith Thoresz
• Suan Yong
• April 6, 1999

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Papers

• J. Hodgins, W. Wooten, D. Brogan, and J. O'Brien.
  Animating Human Athletics. SIGGRAPH '95.
• J. Hodgins and N. Pollard, 1997. Adapting
  Simulated Behaviors For New Characters, SIGGRAPH
  97 Proceedings, Los Angeles, CA.
• Bruderlin and Calvert. Goal-Directed, Dynamic
  Animation of Human Walking. Proceedings SIGGRAPH
  '89.
• Lee, Wei, Zhao, and Badler. Strength Guided
  Motion. SIGGRAPH '90.
• Phillips and Badler. Interactive Behaviors for
  Bipedal Articulated Figures. SIGGRAPH '91.
• N. Badler, R. Bindiganavale, J. Bourne, J.
  Allbeck, J. Shi and M. Palmer. "Real time virtual
  humans," International Conference on Digital
  Media Futures, Bradford, UK, April 1999.

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Why simulating humans is useful

• Ergonomic prototyping
• Virtual conferencing
• Interaction in graphical worlds
• Games
• Education
• Training
• Military/Space/Whatever simulation

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Background

• Biomechanics
• data for creating dynamic models and motions
• Robotics
• control strategies
• Computer graphics
• implementation experience

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Difficulties in Animating Humans

• Natural motions almost impossible to create
  computationally (the problem)
• Large search spaces for underconstrained
  scenarios
• Physical realism requires complex models
• Fine control vs. tedious manual work
• How to specify controls/constraints intuitively

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Animating Techniques

• Keyframing
• detailed control
• tedious
• Procedural Methods
• can be physically correct, high-level control
• unnatural motions, difficult to create
• Motion Capture
• natural motions
• inflexible

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Procedural methods

• High-level control
• specifying desired motion
• Control Algorithms
• control the primary actions (choreography)
• Low-level Procedures
• generates the motion (kinematics)

vault
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Procedural methods

• High-level control
• specifying desired motion
• Control Algorithms
• control the primary actions (choreography)
• Low-level Procedures
• generates the motion (kinematics)

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Procedural methods

• High-level control
• specifying desired motion
• Control Algorithms
• control the primary actions (choreography)
• Low-level Procedures
• generates the motion (kinematics)

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Animating Human Athletics(Hodgins et al)

• Dynamic simulation of human motion
• Running
• Cycling
• Vaulting
• Control algorithms
• state machines that describe each specific motion
• Toolbox of motions (control algorithms)

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Control Algorithms

• Control the primary actions using equations for
  motion
• Basic process (for each time step)
• calculate joint positions and velocities
• compute joint torque (with proportional-derivative
  servos)
• integrate equations of motion
• Hand designed and tuned

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Control Algorithms

• state machines connecting phase of behavior to
  active control laws

flight
ball of foot leaves ground
heel touches ground
loading
unloading
knee bends
knee extended
heel contact
toe contact
hip in front of heel
ball of foot touches ground
heel/toe contact
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Example Running

• Ground speed matching
• reduces disturbance due to foot touchdown
• Hand tuning of arms to produce natural looking
  gait
• Control algorithms modified (by hand) when path
  is a curve
• User-specified input
• forward velocity
• desired path

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Manual vs. Automatic Generation of Control
Algorithms

• Manual
• requires vast knowledge of control techniques,
  human dynamics, etc.
• tedious
• Automatic
• reduces animators work
• expensive, harder to implement, impractical
• usually lacks natural look

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Summary

• Advantages
• produce physically correct/realistic motions
• easy to create simulated motion
• can easily create similar motions
• Disadvantages
• robust algorithms difficult to create
• require detailed knowledge of the system
• computational expense grows with constraints
• generally accurate only for one complete action

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Adapting Simulated Behaviors(Hodgins et al)

• Goal fit a simulated motion from one model to
  another
• simulated motion represented as control
  algorithms
• This is not a trivial task
• models may have different geometries
• simple geometric scaling is not enough

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Basic Method

• Approximate new control system
• scale control parameters (e.g. size, masses,
  moments etc)
• Fine-tune control system
• search for a control system with good
  steady-state behavior
• use simulated annealing

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Scaling

• Geometric scaling
• joint angles, position/orientation, forward
  velocity, etc
• Good scale ratio must be found (e.g. leg length
  for running)
• Mass Scaling
• requires selection of relevant body segments
  (based on knowledge of behavior)

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Tuning

• Implemented as a search over the reduced space.
• Optimization Criteria ground speed matching,
  body pitch, timing of thrust, extension of ankle
  and knee
• Search space contains large number of local
  minima
• use simulated annealing
• Tuning done in steps of different scales to
  reduce step sizes

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Goal-directed Animation(Bruderlin et al)

• Humans and animals are goal-oriented
• motions are specified as goals, then translated
  into joint movements, etc.
• Idea Combine dynamic motion control with
  goal-directed motion control
• simplifies the work of animating
• less detail needed to define a motion compared to
  keyframing

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Keyframe-Less Animation of Walking(KLAW)

• Levels of control
• Desired motion (goal) high-level control
• Control algorithms (kinematics) and gait
  refinements
• Motion equations are Lagrangian
• Dynamic model assumes constant segment masses and
  symmetrical segments

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High Level Control

• Three fundamental locomotion parameters
• forward velocity
• step length
• step frequency
• Decomposed into state-phase timings and symmetry
  of steps
• Passed as step constraints to low-level control

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Low-level Control

• Motion broken up into stance and swing phases

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Strength-guided Motion(Lee et al)

• Idea Use strength, comfort, and perceived
  exertion as heuristics for optimizing movement

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Basic Approach

• strength model used as optimality criterion for
  control algorithms and path decisions.
• comfort region dictated by muscular strength
• task paths chosen by system, not animator (i.e.
  keyframing)
• plans short paths toward the goal based on
  desired action

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Problem specification

• Comfort level for each joint given by max torque
  ratio (current torque divided by max torque for
  current position and velocity)
• Perceived exertion expected level of difficulty
  in completing a task perception of amount of
  strength required
• Strength model maximum achievable joint torque
  based on muscle groups
• Muscle group strength depends on body position,
  gender, handedness, fatigue, etc

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System Design
comfort, perceived exertion, etc

• Condition Monitor monitors body state
  (positions, max strength, torques, etc.) and
  suggests motion strategies to PPS
• Path Planning Scheme (PPS) plans end effector
  movements
• must not violate strength constraints
• tradeoff between reaching goal and avoiding
  straining the model
• Rate Control Process (RCP) determines joint
  rates for motion

Condition Monitor
Path Planning Scheme
Rate Control Process
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Motion Strategies

• Available torque (available strength) people
  tend to move stronger joint.
• Reducing moment avoids further stress while
  trying to reach goal (increases available torque)
• Pull back retract when a joint reaches max
  strength leads to a stable configuration
  (posture that a set of joints should form in
  order to withstand large forces)
• Recoil and jerk similar to a weight lifter
  recoiling legs jerk reduces forces necessary to
  complete a task for the set of active joints

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Interactive Behaviors(Phillips, Badler)

• Approach
• Specify constraints on parts of the figure
• Constraints determine end-effector positions
• Use Inverse Kinematics to computes motion (joint
  angles)
• Important constraints identified for bipedal
  articulated figures
• the feet position relative to the ground
• center of mass and balance to maintain balance

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Real-time Virtual Humans(Badler et al)

• Idea Motions for animated humans can be
  described at a high-level using natural language.
• Scenes and motions can be more complicated if
  computed in parallel.

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Goals

• What should Virtual Humans be capable of doing?
• Playing a stored motion sequence
• Posture changes and balance adjustments
• Reaching, grasping, locomoting, looking
• Facial expressions
• Physical force- or torque-induced movements
  (jumping, falling, swinging)
• Blending (coarticulating) one movement into the
  next one

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Specifying Actions

• Parameterized Action Representation (PAR)
• Natural language representation for specifying
  motions and dynamics
• Parameterized because the action depends on its
  participants (agents, object, etc.)
• Output fed to PaT-Net

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Performing Simultaneous Actions

• Parallel Transition Networks (PaT-Nets)
• Provides a non-linear animation model that
  enables simultaneous control over body motions as
  well as interaction between characters and their
  environments.
• Effective, but must be hand-coded in Lisp or C.

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Conclusions

• The state of the art still falls short of
  expectations
• Procedural methods for creating human motion are
  difficult to design, and seldom look realistic
• Control algorithms are specific to one action,
  and must be recoded for new actions
• actions that seem related, e.g. walking and
  running, are physically very different
• automatic methods exist, but hand coding produces
  more natural looking results

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Open Questions

• Transitioning between unrelated motions
• e.g. between walk and run
• What are the characteristics of human motion that
  current systems are unable to simulate?
• is this worth pursuing?
• is Motion Capture a more viable alternative?
• How to simulate high-level behaviors such as
  personality?

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The End