Abstract:
We describe how to synthesize, automatically, motion controllers for locomotive tasks involving animated characters modeled as 3D mass-spring lattices. The motion controllers determine an actuation sequence based on elapsed time, not physical state; actuation is represented economically using a small, predefined set of global lattice deformations; and stochastic search is used to determine effective values for the controller parameters. Our algorithm generates controllers that produce attractive, visually plausible motion for simple locomotive tasks in under an hour on a standard workstation, which is more than an order of magnitude faster than comparable approaches to motion synthesis for 3D articulated-linkage models. Key Words: animation, motion synthesis, controller synthesis, heuristic methods, stochastic optimization, mass-spring models. 1 Introduction Motion synthesis is the task of automatically generating visually plausible movement of an animated character that conforms to th...
Citations
|
1152
|
Numerical Recipes in C: The Art of Scientific Computing
– Press, Teukolskey, et al.
- 2002
|
|
223
|
Simulating Humans: Computer Graphics, Animation and Control
– Badler, Philips, et al.
- 1993
|
|
222
|
Evolving virtual creatures
– Sims
- 1994
|
|
203
|
Animating Human Athletics
– Hodgins, Wooten, et al.
- 1995
|
|
186
|
Evolving dynamical neural networks for adaptive behavior
– Beer, Gallagher
- 1992
|
|
180
|
Learning to coordinate behaviors
– Maes, Brooks
- 1990
|
|
135
|
Artificial fishes: physics, locomotion, perception, behavior
– Tu, Terzopoulos
- 1994
|
|
131
|
Realistic animation of rigid bodies
– HAHN
- 1988
|
|
131
|
Principles of traditional animation applied to 3d computer animation. Computer Graphics
– Lasseter
- 1987
|
|
119
|
Good Vibrations : Modal Dynamics for Graphics and Animation
– Pentland, Williams
- 1989
|
|
99
|
Planning Motions with Intentions
– Koga
|
|
95
|
Animation of Dynamic Legged Locomotion
– Raibert, Hodgins
- 1991
|
|
83
|
Spacetime constraint revisited
– JT, Marks
- 1993
|
|
76
|
Sensor-actuator networks
– Panne, Fiume
- 1993
|
|
68
|
The motion dynamics of snakes and worms
– Miller
- 1988
|
|
50
|
On-line computational scheme for mechanical manipulators
– Luh, Walker, et al.
- 1980
|
|
48
|
Automated learning of muscleactuated locomotion through control abstraction
– Grzeszczuk, Terzopoulos
- 1995
|
|
30
|
Genetic programming : building artificial nervous systems using genetically programmed neural networks modules
– Garis
- 1990
|
|
28
|
Automatic programming of robots using genetic programming
– Koza, Rice
- 1992
|
|
26
|
Virtual wind-up toys for animation
– Panne, Kim, et al.
- 1994
|
|
19
|
A parameter optimization approach for the optimal control of large-scale musculoskeletal systems
– PANDY, ANDERSON, et al.
- 1992
|
|
17
|
Further experience with controller-based automatic motion synthesis for articulated "gures
– Auslander, Fukunaga, et al.
- 1995
|
|
17
|
de Panne. Parameterized gait synthesis
– van
- 1996
|
|
16
|
Connectionist Modelling of Skill Dynamics
– Ridsdale
- 1990
|
|
11
|
Automatic control of physically realistic animated figures using evolutionary programming
– Fukunaga, Marks
- 1994
|
|
5
|
Control parameterization: A unified approach to optimal control problems with general constraints
– Goh, Teo
- 1988
|
