Choosing a single latent layer for GAN inversion leads to a dilemma between obtaining a faithful reconstruction of the input image and being able to perform downstream edits (1st and 2nd row). In contrast, our proposed method automatically selects the latent space tailored for each region to balance the reconstruction quality and editability (3rd row).

Existing GAN inversion and editing methods work well for aligned objects with a clean background, such as portraits and animal faces, but often struggle for more difficult categories with complex scene layouts and object occlusions, such as cars, animals, and outdoor images. We propose a new method to invert and edit such complex images in the latent space of GANs, such as StyleGAN2. Our key idea is to explore inversion with a collection of layers, spatially adapting the inversion process to the difficulty of the image. We learn to predict the "invertibility" of different image segments and project each segment into a latent layer. Easier regions can be inverted into an earlier layer in the generator's latent space, while more challenging regions can be inverted into a later feature space. Experiments show that our method obtains better inversion results compared to the recent approaches on complex categories, while maintaining downstream editability.

Different Latent Spaces

Results

Input Image:

Cars

Comparing Final Inversion

SAM Inversion

W+ Inversion

Input W+ Inversion SAM Inversion

Better
Editing Better
Inversion

Invertibility Map Used

W+

F4

F6

F8

F10

Edit Name #1: "Blue car"

Edit Name #2: "Ford Model T"

Gaurav Parmar, Yijun Li, Jingwan Lu, Richard Zhang, Jun-Yan Zhu, Krishna Kumar Singh Spatially-Adaptive Multilayer Selection for GAN Inversion and Editing CVPR, 2022.