Generative adversarial networks (GANs) synthesize realistic images from random latent vectors. Although manipulating the latent vectors controls the synthesized outputs, editing real images with GANs suffers from i) time-consuming optimization for projecting real images to the latent vectors, ii) or inaccurate embedding through an encoder. We propose StyleMapGAN: the intermediate latent space has spatial dimensions, and a spatially variant modulation replaces AdaIN. It makes the embedding through an encoder more accurate than existing optimization-based methods while maintaining the properties of GANs. Experimental results demonstrate that our method significantly outperforms state-of-the-art models in various image manipulation tasks such as local editing and image interpolation. Last but not least, conventional editing methods on GANs are still valid on our StyleMapGAN. Source code is available at https://github.com/naver-ai/StyleMapGAN.

通过在中间的潜空间使用具有空间维度的调制替换AdaIN，我们提出了StyleMapGAN，它使得通过编码器的嵌入比现有基于优化方法更加准确，同时保持GAN的属性。实验结果表明，我们的方法在各种图像操作任务中显著优于最先进的模型。

GAN中潜在空间维度的利用，用于实时图像编辑