1. Synthesizing Realistic Human Motion
by Greg Coombe

2. Applications Games/Special FX Virtual Environments Ergonomics/Design
Military/Medical Simulations

3. Motivation
People are skilled at perceiving the subtle details of human motion.
We can identify friends by the style of their walk, even from far away.
To be compelling, synthesized humans in computer animations and virtual environments must have realistic motion.
from Hodgins

4. Human Motion Data Three major techniques Advantages of Simulation
Motion Capture
Animators
Advantages of Simulation
Greater flexibility
Interaction with virtual environment
Secondary motion (hair, clothes, etc.)

5. Controlling Virtual Characters
from Badler

6. Control - Language
High-level specifications are often in the form of a language
ex:
Push
Neck joint
Nod joint draw_head
Lchest joint
Lshoulder joint
Lelbow joint
Lwrist joint draw_arm
-1,1,1 scale
Rchest joint
Rshoulder joint
Relbow joint
Rwrist joint draw_arm
Pop
Waist draw_torso
Lpelvis joint
Lhip joint
Lankle joint 1 draw_leg
from Jack

7. Control - Keyframe Keyframing, with physical simulation between frames
Knowledge-Driven, Interactive Animation of Human Running, Brunderin, A.

8. Control - Operators
Small programs, called operators, perform simple tasks.
Ex. Balance, Lift Leg, Turn Head, etc.
Complex, human-like behavior from applying multiple operators.
Animators usually connect together operators, and supply transitions

9. How do you do this? Need: Accurate models Flexible control strategies
Mass/inertia derived from biomedical literature
DOF for joints based on bone/tendon/ligament structure
Flexible control strategies
High-level, mathematically accurate (“good math makes good simulations”)

10. Papers
Animating Human Athletics. Hodgins, J. K., Wooten, W. L., Brogan, D. C., O'Brien, J. F. SIGGRAPH '95.
Animation of Dynamic Legged Locomotion. Raibert, M., and Hodgins, J. SIGGRAPH `91.
Transitions Between Dynamically Simulated Motions: Leaping, Tumbling, Landing, and Balancing. Wooten, W. L., Hodgins, J. K., 1997.
Adapting Simulated Behaviors For New Characters. Hodgins, J. K. and Pollard, N. S., SIGGRAPH 1997

11. Some running movies…

12. Human Model
The human model is a set of rigid links connected by rotary joints. These joints have simplified DOF based on skeletal structure.

13. Control - Operators
The joints are controlled by a simple Finite State Machine.

14. Finite State Machine Each state has equations governing its behavior.
ex. When the foot touches the ground, the desired distance from the hip to the heel is:
ts – estimated time to contact
Θ – angle of runner on ground
lf – length of foot
x’, y’ – velocity
xd’, yd’ – desired velocity

15. How are forces applied? Torque forces Constraint forces
Each internal joint has a simplified muscle model, a “torque source”.
Constraint forces
Points of contact (such as feet & ground) are modeled with constraints.

16. Proportional Derivative Servos
Not all behavior encapsulated by these equations.
Much of human motion is just trying to compensate for forces. Examples include trying to balance, swinging arms when running, etc.
Use proportional-derivative servos to control the other limbs (such as arms and hips).
This is like correcting your car direction while driving; first steer towards the lane, then back off as you get closer.
Raibert, M., and Hodgins, J., "Animation of Dynamic Legged Locomotion," SIGGRAPH `91

17. Where did these ideas come from?
MIT Leg Lab, Raibert showed robotic running could be accomplished using a few simple, de-coupled control laws.

18. How does this compare?
In the paper, some effort is spent trying to evaluate the correctness of these models.
There is a later paper (Judgments of Human Motion with Different Geometric Models, IEEE: Transactions on Visualization and Computer Graphics, 1998) where a more rigorous comparison is performed.

19. How does this compare?
Simulated Runner
Real Runner

20. Some vaulting movies…

21. Closeup

22. Transitions
These systems work well for simple, repeated motions like running or bicycling.
But, equations are pretty specific to these motions.
What about more complex behavior?
Chain together several simple actions
Need to handle transitions between actions

23. Transitions
Wooten, W. L., Hodgins, J. K., Transitions Between Dynamically Simulated Motions: Leaping, Tumbling, Landing, and Balancing.

24. Scaling
Can we adapt the running simulation to humans of different sizes?
Need to scale the control algorithms and forces to account for different limb lengths, masses, and inertial moments.
Adapting Simulated Behaviors For New Characters
Hodgins, J. K. and Pollard, N. S., SIGGRAPH 1997.

25. Scaling
Two stages:
Control system parameters are scaled based on the size and moment of inertia of the dynamic models for the new and the old actors.
A subset of the parameters is fine-tuned using a search process based on simulated annealing.

26. Implementation These videos look good. Can I go implement this myself?
Maybe, but “observations of human runners were used to tune the parameters to produce a natural-looking gait.”
That is, this requires a lot of hand-tuning.

27. References
Virtual Humans: Behaviors and Physics, Acting and Reacting. Siggraph Course 28, 1998.
GVU Animation Lab.
UPenn Center for Human Modeling and Simulation