Comparison of CoModGans, LaMa and GLIDE for Art Inpainting Completing M.C Escher's Print Gallery

Lucia Cipolina-Kun, Simone Caenazzo, Gaston Mazzei; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2022, pp. 716-724

Abstract

Digital art restoration has benefited from inpainting models to correct the degradation or missing sections of a painting. This work compares three current state-of-the art models for inpainting of large missing regions. We provide qualitative and quantitative comparison of the performance of CoModGans, LaMa and GLIDE in inpainting blurry and missing sections of images. We use Escher's incomplete painting Print Gallery as our test study since it presents several of the challenges commonly present in restorative inpainting.

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[bibtex]
@InProceedings{Cipolina-Kun_2022_CVPR, author = {Cipolina-Kun, Lucia and Caenazzo, Simone and Mazzei, Gaston}, title = {Comparison of CoModGans, LaMa and GLIDE for Art Inpainting Completing M.C Escher's Print Gallery}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}, month = {June}, year = {2022}, pages = {716-724} }