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基于改进CycleGAN的人脸卡通风格化迁移

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利用改进的循环生成对抗网络 CycleGAN 实现人脸卡通风格化迁移.文中模型在CycleGAN基础上通过对生成器模型进行结构改进,在编码器部分采用稠密卷积结构,使模型在减少了参数量的同时可以更好地关注人脸细节特征,加强特征传播,在不改变个人脸型的基础上实现人脸卡通风格化迁移.实验结果表明,改进后的模型风格化迁移图像分辨率更高,配色更协调,尤其是细节处如眼睛、发丝等卡通迁移效果更流畅.
Face cartoon style transfer based on improved CycleGAN
Face cartoon stylised transfer using improved CycleGAN.The model in this paper is based on CycleGAN by making structural improvements to the generator model.A dense convolutional structure is used in the encoder part,so that the model can better focus on the detailed features of the face and enhance the feature propagation while reducing the number of parameters.Achieve cartoon stylised transfer of faces without changing the individual's face shape.The results of the comparison experiments show that the improved model stylized transfer image has higher resolution and more coordinated colour scheme,especially the details such as eyes,hair and other cartoon transfer effect is smoother.

style transferCycleGANDenseNet modelencoder

杜润梅、李旭辉、刘铭

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长春工业大学 数学与统计学院,吉林 长春 130012

风格迁移 CycleGAN DenseNet模型 编码器

吉林省发改委基本建设资金项目吉林省自然科学基金项目

2022C043-220200201157JC

2024

长春工业大学学报
长春工业大学

长春工业大学学报

影响因子:0.282
ISSN:1674-1374
年,卷(期):2024.45(3)