Make It So: Steering StyleGAN for Any Image Inversion and Editing

Autor:	Bhattad, Anand, Shah, Viraj, Hoiem, Derek, Forsyth, D. A.
Rok vydání:	2023
Předmět:	Computer Science - Computer Vision and Pattern Recognition Computer Science - Graphics Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	StyleGAN's disentangled style representation enables powerful image editing by manipulating the latent variables, but accurately mapping real-world images to their latent variables (GAN inversion) remains a challenge. Existing GAN inversion methods struggle to maintain editing directions and produce realistic results. To address these limitations, we propose Make It So, a novel GAN inversion method that operates in the $\mathcal{Z}$ (noise) space rather than the typical $\mathcal{W}$ (latent style) space. Make It So preserves editing capabilities, even for out-of-domain images. This is a crucial property that was overlooked in prior methods. Our quantitative evaluations demonstrate that Make It So outperforms the state-of-the-art method PTI~\cite{roich2021pivotal} by a factor of five in inversion accuracy and achieves ten times better edit quality for complex indoor scenes. Comment: project: https://anandbhattad.github.io/makeitso/
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2304.14403 Zobrazit plný text záznamu View this record from Arxiv