基于StyleGAN2的京剧脸谱图像自动生成

Xiaoyu Xin, Yinghua Shen, Rui Xiong, Xiahan Lin, Ming Yan, Wei Jiang
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引用次数: 0

摘要

图像生成技术经常用于智能图像生成的各种应用中,它可以学习真实图像的特征分布,并从分布中进行采样,从而获得高保真度的生成图像。本文主要研究具有中国文化特色的京剧脸谱的特征提取和智能生成技术。基于京剧人脸数据集的创建,本文比较了生成对抗网络(StyleGAN2)基于风格的生成器架构的不同变体和不同数据集大小对人脸生成质量的影响。实验结果表明,当数据集较小且分布不平衡时,StyleGAN2添加自适应判别器增强(Adaptive Discriminator Augmentation, ADA)模块生成的合成图像视觉效果更好,具有良好的局部随机性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Automatic Image Generation of Peking Opera Face using StyleGAN2
Image generation technology, which is often used in various applications of intelligent image generation, can learn the feature distribution of real images and sample from the distribution to obtain the generated images with high fidelity. This paper focuses on the feature extraction and intelligent generation techniques of Peking opera face with Chinese cultural characteristics. Based on the creation of a Peking opera face dataset, this paper compares the impact of different variants of a Style-based generator architecture for Generative Adversarial Networks (StyleGAN2) and different sizes of datasets on the quality of face generation. The experimental results verify that the synthetic images generated by StyleGAN2 with the addition of the Adaptive Discriminator Augmentation (ADA) module are visually better and have good local randomness when the dataset is small and unbalanced in distribution.
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