# Faster Unsupervised Semantic Inpainting: A GAN Based Approach

## TL;DR

This paper introduces a faster GAN-based unsupervised semantic inpainting method that improves inference speed and visual quality for both images and videos by better initialization and considers temporal cues for video inpainting.

## Contribution

It presents a novel initialization approach that significantly accelerates GAN-based inpainting and is the first to incorporate temporal cues for video inpainting.

## Key findings

- 4.5-5x faster inference on images
- 80x faster inference on videos
- Improved spatial and temporal reconstruction quality

## Abstract

In this paper, we propose to improve the inference speed and visual quality of contemporary baseline of Generative Adversarial Networks (GAN) based unsupervised semantic inpainting. This is made possible with better initialization of the core iterative optimization involved in the framework. To our best knowledge, this is also the first attempt of GAN based video inpainting with consideration to temporal cues. On single image inpainting, we achieve about 4.5-5$\times$ speedup and 80$\times$ on videos compared to baseline. Simultaneously, our method has better spatial and temporal reconstruction qualities as found on three image and one video dataset.