论文标题
时尚编辑：揭示甘恩斯的本地语义
Editing in Style: Uncovering the Local Semantics of GANs
论文作者
论文摘要
尽管近年来GAN图像合成的质量大大提高，但我们控制和调理输出的能力仍然有限。为了专注于StyleGan，我们引入了一种简单有效的方法，将本地语义意识的编辑制作到目标输出图像。这是通过从源图像（也是GAN输出）借用的样式向量操纵来实现的。我们的方法不需要外部模型的监督，也不需要涉及复杂的空间变形操作。取而代之的是，它依赖于Stylegan在培训期间学习的语义对象的新兴语义对象。语义编辑在产生人的面孔，室内场景，猫和汽车上证明了语义编辑。我们测量了我们方法产生的编辑的局部性和光真实性，并发现它两者兼而有之。
While the quality of GAN image synthesis has improved tremendously in recent years, our ability to control and condition the output is still limited. Focusing on StyleGAN, we introduce a simple and effective method for making local, semantically-aware edits to a target output image. This is accomplished by borrowing elements from a source image, also a GAN output, via a novel manipulation of style vectors. Our method requires neither supervision from an external model, nor involves complex spatial morphing operations. Instead, it relies on the emergent disentanglement of semantic objects that is learned by StyleGAN during its training. Semantic editing is demonstrated on GANs producing human faces, indoor scenes, cats, and cars. We measure the locality and photorealism of the edits produced by our method, and find that it accomplishes both.
