Multi-Modality Image Inpainting using Generative Adversarial Networks

TL;DR

This paper introduces a novel GAN-based model that simultaneously performs multi-modality image inpainting and image-to-image translation, addressing a previously unexplored combination of these tasks with promising results.

Contribution

The paper presents a new model that combines image inpainting with multi-modality image translation, filling a gap in existing research.

Findings

Effective inpainting and translation on night-to-day images

Qualitative and quantitative results show promising performance

Addresses a previously unexplored combined task

Abstract

Deep learning techniques, especially Generative Adversarial Networks (GANs) have significantly improved image inpainting and image-to-image translation tasks over the past few years. To the best of our knowledge, the problem of combining the image inpainting task with the multi-modality image-to-image translation remains intact. In this paper, we propose a model to address this problem. The model will be evaluated on combined night-to-day image translation and inpainting, along with promising qualitative and quantitative results.