WaGI : Wavelet-based GAN Inversion for Preserving High-frequency Image Details. (arXiv:2210.09655v1 [cs.CV])

Recent GAN inversion models focus on preserving image-specific details
through various methods, e.g., generator tuning or feature mixing. While those
are helpful for preserving details compared to a naiive low-rate latent
inversion, they still fail to maintain high-frequency features precisely. In
this paper, we point out that the existing GAN inversion models have inherent
limitations in both structural and training aspects, which preclude the
delicate reconstruction of high-frequency features. Especially, we prove that
the widely-used loss term in GAN inversion, …

arxiv gan image wavelet