Spatial-Contextual Discrepancy Information Compensation for GAN
  Inversion
Ziqiang Zhang Yan Yan Jing-Hao Xue Hanzi Wang
Most existing GAN inversion methods either achieve accurate reconstruction
but lack editability or offer strong editability at the cost of fidelity.
Hence, how to balance the distortioneditability trade-off is a significant
challenge for GAN inversion. To address this challenge, we introduce a novel
spatial-contextual discrepancy information compensationbased GAN-inversion
method (SDIC), which consists of a discrepancy information prediction network
(DIPN) and a discrepancy information compensation network (DICN). SDIC follows
a "compensate-and-edit" paradigm and successfully bridges the gap in image
details between the original image and the reconstructed/edited image. On the
one hand, DIPN encodes the multi-level spatial-contextual information of the
original and initial reconstructed images and then predicts a
spatial-contextual guided discrepancy map with two hourglass modules. In this
way, a reliable discrepancy map that models the contextual relationship and
captures finegrained image details is learned. On the other hand, DICN
incorporates the predicted discrepancy information into both the latent code
and the GAN generator with different transformations, generating high-quality
reconstructed/edited images. This effectively compensates for the loss of image
details during GAN inversion. Both quantitative and qualitative experiments
demonstrate that our proposed method achieves the excellent
distortion-editability trade-off at a fast inference speed for both image
inversion and editing tasks.