论文标题
样式空间中的多方向子空间编辑
Multi-Directional Subspace Editing in Style-Space
论文作者
论文摘要
本文介绍了一种新技术，用于在Stylegan的潜在空间中查找分离的语义方向。我们的方法确定了有意义的正交子空间，这些子空间允许编辑一个人的脸部属性，同时最大程度地减少了其他属性的不希望变化。我们的模型能够在多个方向上编辑单个属性，从而产生一系列可能的生成图像。我们将我们的计划与三种最先进的模型进行了比较，并表明我们的方法在面部编辑和解开功能方面优于它们。此外，我们建议对评估属性分离和分离的定量度量，并在这些措施方面表现出我们模型的优越性。
This paper describes a new technique for finding disentangled semantic directions in the latent space of StyleGAN. Our method identifies meaningful orthogonal subspaces that allow editing of one human face attribute, while minimizing undesired changes in other attributes. Our model is capable of editing a single attribute in multiple directions, resulting in a range of possible generated images. We compare our scheme with three state-of-the-art models and show that our method outperforms them in terms of face editing and disentanglement capabilities. Additionally, we suggest quantitative measures for evaluating attribute separation and disentanglement, and exhibit the superiority of our model with respect to those measures.
