Study on Semantic Inpainting Deep Learning Models for Artefacts with Traditional Motifs

This paper proposes the use of pre-trained semantic inpainting deep learning architectures to reach a high-fidelity, visually plausible filling content suggestion for the restoration of museum textile objects with traditional motifs. Two state-of-the-art models are selected and their reconstructions are additionally given to an autoencoder trained on a specific collection of textiles. The results show some potential of the tandem and the viability of an automatic support for artefact restoration.