Cartoon Character Generation using Generative Adversarial Network
Gourab Guruprasad1 , Gauri Gakhar2,D Vanusha3
1Gourab Guruprasad , SRM Institute of Science and Technology Tamil Nadu.
2Gauri Gakhar , SRM Institute of Science and Technology Tamil Nadu.
3D Vanusha, SRM Institute of Science and Technology Tamil Nadu.
Manuscript received on April 02, 2020. | Revised Manuscript received on April 15, 2020. | Manuscript published on May 30, 2020. | PP: 1-4 | Volume-9 Issue-1, May 2020. | Retrieval Number: F7639038620/2020©BEIESP | DOI: 10.35940/ijrte.F7639.059120
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Animated faces show up in cartoons, comics and games. They are broadly utilized as profile pictures in online life stages, for example, Facebook and Instagram. Drawing an animated face is work intense. Not just it requires proficient skills but also its time consuming. A lot of time is wasted in creating a cartoon character from scratch , and most of the cases ends up in creating an awkward character having very low polygon intensity. Generative adversarial network (GAN) framework can be trained with a collection of cartoon images. GANs comprises of a generator network and a discriminator network. Because of the ability of deep networks and the competitive training algorithm, GANs produce realistic images, and have great potential in the field of image processing. This method turns out to be a surprisingly handy tool in enhancing blurry images. The underlying idea behind GAN is that it contains two neural networks that compete with each other in a zero-sum game, which constitutes of generator and a discriminator.
Keywords: Generative Adversarial Network
Scope of the Article: Next Generation Internet & Web Architectures.