基于改进CycleGAN的人脸剪纸自动生成

针对人脸剪纸手工设计难度大, 制作周期长等问题, 本文首次利用生成对抗网络生成高质量人脸剪纸. 面向人脸剪纸艺术特点, 提出了一种基于CycleGAN的改进网络: 1) 在原始CycleGAN生成器中引入CBAM注意力模块, 增强网络特征提取能力; 2) 引入针对鼻、眼、唇等关键面部区域的局部鉴别器, 提升人脸剪纸中以上区域的生成效果; 3)设计基于图像边缘信息与SSIM的损失函数, 取代CycleGAN的前向循环一致损失, 消除所得人脸剪纸中的阴影. 相较于其他人脸剪纸自动生成方法, 本文方法可快速生成与原始人脸相似度高、线条连续流畅、具有艺术美感的人脸剪纸. 此外, 本文还提出了一种人脸剪纸连通性后处理方法, 使所得结果更符合中国传统剪纸整体连通的特点.

Automatic Portrait Paper-cut Generation Based on Improved CycleGAN

Aiming at the difficulty in manually designing portrait paper-cuts, this study employs the generative adversarial network (GAN) to generate high-quality portrait paper-cuts for the first time. Based on the artistic characteristics of portrait paper-cuts, an improved network based on CycleGAN is proposed. 1) The CBAM attention module is introduced into the CycleGAN generator to enhance the feature extraction of the network. 2) The local discriminator for key facial regions such as nose, eyes, and lips is introduced to improve the generation effect of the above areas in generated portrait paper-cuts. 3) A new loss function is designed based on image edge information and SSIM, which will be adopted to replace the original forward cycle-consistency loss of CycleGAN and eliminate the shadows in the portrait paper-cuts. Compared with other automatic generation methods of portrait paper-cuts, the proposed method can quickly generate paper-cuts featuring high similarity to the original human face, continuous and smooth lines, and aesthetic beauty. Additionally, this study also puts forward a post-processing method of portrait paper-cut connectivity to make the obtained results more consistent with the overall connectivity of traditional Chinese paper-cuts.