使用深度神经网络的日本动画风格转移

Shiyang Ye, Ryo Ohtera
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摘要

本文试图找到一种深度学习的方法来实现日本动画风格的转换,从而更容易、更高效地创建动画背景图像,从而降低日本动画产业的成本和工作量。我们现在构建的方法是基于卷积神经网络(CNN)的深度学习方法,卷积神经网络在图像处理任务中最流行。此外,我们限制了输出图像中使用的颜色的数量,以接近理想的颜色。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Japanese Animation Style Transfer Using Deep Neural Networks
This paper tries to find a deep-learning approach to Japanese Animation style transfer that creates animation background images more easily and efficiently so that we can reduce the cost and workload for Japanese animation industry. Our approach now builds is based on a deep-learning approach using Convolutional Neural Networks(CNN) which is most popular in image processing tasks. Moreover, we limit the number of the color used in the output images to get close to an ideal color.
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