Cheongju University, Cheongju 28503, Republic of Korea.
Comput Intell Neurosci. 2022 Aug 20;2022:3146488. doi: 10.1155/2022/3146488. eCollection 2022.
In recent years, for China, animation industry is a relatively new and mature emerging national sunrise industry after animation industry, which appears on the world stage more and more frequently and is widely concerned and valued by people from all over the world. Therefore, this paper innovatively uses the convolutional neural network algorithm to innovate the color matching generation of animation characters and improve the traditional technology of color matching for animation characters. In this paper, we mainly use Generative Adversarial Network (GAN), Deep Convolutional Generative Adversarial Network and VGG model, and multiscale discriminator theory and use ACGAN research method. And we study this paper's innovative LMV-ACGAN research method, and we have come to the conclusion that other models have higher collapse rate than this model; this model has higher color matching of anime characters. Color matching improves with the increase of convolutional neural network utilization, etc. Moreover, superior and minor reviews of this study are provided to make later researchers understand this study more rationally and objectively.
近年来，对于中国而言，动画产业是继动画产业之后一个相对较新且成熟的新兴民族朝阳产业，在世界舞台上越来越频繁地出现，并受到来自世界各地的人们的广泛关注和重视。因此，本文创新性地使用卷积神经网络算法对动画角色的配色生成进行创新，改进了动画角色的传统配色技术。本文主要使用生成对抗网络（GAN）、深度卷积生成对抗网络和 VGG 模型，以及多尺度判别器理论和使用 ACGAN 研究方法。我们研究了本文的创新 LMV-ACGAN 研究方法，并得出结论，其他模型的崩溃率高于此模型；此模型的动画角色颜色匹配度更高。颜色匹配随着卷积神经网络利用率的提高而提高等。此外，还提供了本研究的优劣评价，以使后来的研究人员更合理、更客观地了解本研究。
