基于无监督生成对抗网络的人脸素描图像真实化

对于人脸识别验证的研究带动了执法机构和数字娱乐行业将素描转化为真实人脸图像的需求和兴趣.到目前为止,由于网络训练阶段缺乏配对的数据,加上素描与真实照片之间存在着明显的模态差异,现有的方法仍然存在着不可解决的局限性.利用跨域语义一致性损失使输入和输出保持相同的语义信息,并用感知损失替换像素级的循环一致性损失以生成高分辨率...

Unsupervised learning for face sketch-photo synthesis using generative adversarial network

The research in verification of human face issue has impelled the demand and interest of law enforcement agencies and digital entertainment industry in transferring sketches to photo-realistic images. However, sketch-photo synthesis remains a significant challenging problem despite the rapid development of neural networks in image-to-image generation tasks. So far, existing approaches still have inextricable limitations due to the lack of paired data in the training stage and the fact of the striking differences between sketch and photo. To solve this problem, a new framework is proposed to translate face sketches to photo-realistic images in an unsupervised fashion. Compared with current unsupervised image-to-image translation methods, the network leverages an additional semantic consistency loss to keep the input semantic information in the output, and replaces the pixel-wise cycle-consistency with perceptual loss to generate sharper images for face sketch-photo synthesis. This network also employs PGGAN's generator and train it with a GAN loss for realistic output and a cycle consistency loss for driving the same input and output to remain constant. Experiments on two open source data sets verify the effectiveness of our proposal in subjective evaluation and objective standards.