High-fidelity GAN Inversion with Padding Space. (arXiv:2203.11105v2 [cs.CV] UPDATED)

Inverting a Generative Adversarial Network (GAN) facilitates a wide range of
image editing tasks using pre-trained generators. Existing methods typically
employ the latent space of GANs as the inversion space yet observe the
insufficient recovery of spatial details. In this work, we propose to involve
the padding space of the generator to complement the latent space with spatial
information. Concretely, we replace the constant padding (e.g., usually zeros)
used in convolution layers with some instance-aware coefficients. In this way,
the …

arxiv cv fidelity gan padding space