Recent inversion methods have shown that real images can be inverted into
StyleGAN's latent space and numerous edits can be achieved on those images
thanks to the semantically rich feature representations of well-trained GAN
models. However, extensive research has also shown that image inversion is
challenging due to the trade-off between high-fidelity reconstruction and
editability. In this paper, we tackle an even more difficult task, inverting
erased images into GAN's latent space for realistic inpaintings and editings.
Furthermore, by augmenting inverted latent codes with different latent samples,
we achieve diverse inpaintings. Specifically, we propose to learn an encoder
and mixing network to combine encoded features from erased images with
StyleGAN's mapped features from random samples. To encourage the mixing network
to utilize both inputs, we train the networks with generated data via a novel
set-up. We also utilize higher-rate features to prevent color inconsistencies
between the inpainted and unerased parts. We run extensive experiments and
compare our method with state-of-the-art inversion and inpainting methods.
Qualitative metrics and visual comparisons show significant improvements.

利用样式生成对抗网络（StyleGAN）的潜在空间，将真实图像反转并通过语义丰富的特征表示进行多项编辑，进一步扩展到将被擦除的图像反转到 GAN 的潜在空间中进行逼真修复和编辑的更为困难的任务，通过将反转的潜在编码与随机样本的 StyleGAN 映射特征组合，通过训练编码器和混合网络，利用生成的数据来鼓励混合网络同时利用两个输入，并利用高速特征来防止修复部分与未被擦除部分的颜色不一致，并通过与先进的反转和修复方法进行实验和对比，定量指标和视觉比较显示出显著的改进。