论文标题
stylegan潜在空间的语义展开
Semantic Unfolding of StyleGAN Latent Space
论文作者
论文摘要
事实证明，通过倒转和操纵与输入真实图像相对应的潜在代码，生成的对抗网络（GAN）对于图像编辑非常有效。这种编辑属性来自潜在空间的分离性质。在本文中，我们确定面部属性分离不是最佳的，因此依靠线性属性分离的面部编辑是有缺陷的。因此，我们建议通过监督改善语义分解。我们的方法包括使用归一化流量学习代理潜在表示，我们表明这为面部图像编辑提供了更有效的空间。
Generative adversarial networks (GANs) have proven to be surprisingly efficient for image editing by inverting and manipulating the latent code corresponding to an input real image. This editing property emerges from the disentangled nature of the latent space. In this paper, we identify that the facial attribute disentanglement is not optimal, thus facial editing relying on linear attribute separation is flawed. We thus propose to improve semantic disentanglement with supervision. Our method consists in learning a proxy latent representation using normalizing flows, and we show that this leads to a more efficient space for face image editing.
