Mark Ben M. Gurduque and Mary Ludeth J. Tabora. 4 0
GT algorithm (an enhancement of the group based motion algorithm) / 6 6 Mark Ben M. Gurduque and Mary Ludeth J. Tabora. - - - 65 pp. 28 cm. - - - - - . - . - 0 . - . - 0 .
Undergraduate Thesis: (BS in Computer Studies major in Computer Science) -Pamantasan ng Lungsod ng Maynila, 2003.
5
ABSTRACT: The automated animation of human characters continuous to be a challenge in computer graphics. The proponents present a novel kinetic motion planning algorithm for character animation which addresses some of the outstanding problems. The problem domain for the algorithm is as follows: given an environment with designated handholds and foot holds, determine the motion as a optimization problem. The algorithm exploits the time varying of the motion of the animation. It provides a machine learning method on the planner that will enable the animation. It provides a machine learning method on the planner that will enable the animation character movement more realistic and logical. Furthermore, the character movement is more humanlike by decreasing the possibility of having a movement that defies the G-force. It illustrate the results with a demonstration of a human character using walking, running, swinging, climbing in order to navigate in a variety of environments.
5
2 = =
2
2 --0------
6 --0-- 2 --------
0 2 --
--20------
--------20--
--------20--
----2
/ 2
/ 2
/
/
GT algorithm (an enhancement of the group based motion algorithm) / 6 6 Mark Ben M. Gurduque and Mary Ludeth J. Tabora. - - - 65 pp. 28 cm. - - - - - . - . - 0 . - . - 0 .
Undergraduate Thesis: (BS in Computer Studies major in Computer Science) -Pamantasan ng Lungsod ng Maynila, 2003.
5
ABSTRACT: The automated animation of human characters continuous to be a challenge in computer graphics. The proponents present a novel kinetic motion planning algorithm for character animation which addresses some of the outstanding problems. The problem domain for the algorithm is as follows: given an environment with designated handholds and foot holds, determine the motion as a optimization problem. The algorithm exploits the time varying of the motion of the animation. It provides a machine learning method on the planner that will enable the animation. It provides a machine learning method on the planner that will enable the animation character movement more realistic and logical. Furthermore, the character movement is more humanlike by decreasing the possibility of having a movement that defies the G-force. It illustrate the results with a demonstration of a human character using walking, running, swinging, climbing in order to navigate in a variety of environments.
5
2 = =
2
2 --0------
6 --0-- 2 --------
0 2 --
--20------
--------20--
--------20--
----2
/ 2
/ 2
/
/