基于生成对抗网络的卡通人物生成

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引用次数: 0

摘要

动画面孔出现在动画片、漫画和游戏中。它们被广泛用作在线生活阶段的头像,例如Facebook和Instagram。画一张生动的脸是一项紧张的工作。它不仅需要熟练的技能,而且耗时。从零开始创建卡通角色浪费了大量时间,并且大多数情况下最终创造出具有非常低多边形强度的笨拙角色。生成对抗网络(GAN)框架可以用一组卡通图像进行训练。gan由生成器网络和鉴别器网络组成。由于深度网络的能力和训练算法的竞争力,gan可以生成逼真的图像,在图像处理领域具有很大的潜力。这种方法在增强模糊图像时是一种非常方便的工具。GAN背后的基本思想是,它包含两个神经网络,它们在零和游戏中相互竞争,由生成器和鉴别器组成。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Cartoon Character Generation using Generative Adversarial Network
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.
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