论文标题
PA-GAN：面部属性编辑的渐进注意力生成对抗网络
PA-GAN: Progressive Attention Generative Adversarial Network for Facial Attribute Editing
论文作者
论文摘要
面部属性编辑旨在操纵人脸上的属性，例如添加胡须或改变头发颜色。现有的方法在正确的属性生成和保存其他信息（例如身份和背景）之间存在严重的折衷，因为它们编辑了不精确区域中的属性。为了解决这一难题，我们提出了一个渐进式注意力gan（PA-GAN）进行面部属性编辑。在我们的方法中，编辑是从高功能水平逐渐进行的，同时通过每个级别的注意力面罩在适当的属性区域内受到约束。这种方式从一开始就可以防止对无关的区域进行不希望的修改，然后网络可以更多地关注在每个级别的适当边界内正确生成属性。结果，我们的方法与最先进的方法相比，使用无关紧要的细节获得了正确的属性编辑。代码在https://github.com/lynnho/pa-gan-tensorflow上发布。
Facial attribute editing aims to manipulate attributes on the human face, e.g., adding a mustache or changing the hair color. Existing approaches suffer from a serious compromise between correct attribute generation and preservation of the other information such as identity and background, because they edit the attributes in the imprecise area. To resolve this dilemma, we propose a progressive attention GAN (PA-GAN) for facial attribute editing. In our approach, the editing is progressively conducted from high to low feature level while being constrained inside a proper attribute area by an attention mask at each level. This manner prevents undesired modifications to the irrelevant regions from the beginning, and then the network can focus more on correctly generating the attributes within a proper boundary at each level. As a result, our approach achieves correct attribute editing with irrelevant details much better preserved compared with the state-of-the-arts. Codes are released at https://github.com/LynnHo/PA-GAN-Tensorflow.
