GT algorithm (an enhancement of the group based motion algorithm) / Mark Ben M. Gurduque and Mary Ludeth J. Tabora. 6

By: Mark Ben M. Gurduque and Mary Ludeth J. Tabora. 4 0 16, [, ] | [, ] |
Contributor(s): 5 6 [] |
Language: Unknown language code Summary language: Unknown language code Original language: Unknown language code Series: ; March 2003.46Edition: Description: 28 cm. 65 ppContent type: text Media type: unmediated Carrier type: volumeISBN: ISSN: 2Other title: 6 []Uniform titles: | | Related works: 1 40 6 []Subject(s): -- 2 -- 0 -- -- | -- 2 -- 0 -- 6 -- | 2 0 -- | -- -- 20 -- | | -- -- -- -- 20 -- | -- -- -- 20 -- --Genre/Form: -- 2 -- Additional physical formats: DDC classification: | LOC classification: | | 2Other classification:
Contents:
Action note: In: Summary: 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. Other editions:
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Undergraduate Thesis: (BS in Computer Studies major in Computer Science) -Pamantasan ng Lungsod ng Maynila, 2003. 56

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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.

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