Extreme Face Inpainting with Sketch-Guided Conditional GAN

TL;DR

This paper introduces a conditional GAN that uses edge information to improve extreme face inpainting, enabling the recovery of heavily damaged face images with structural guidance.

Contribution

It presents a novel edge-guided conditional GAN architecture that maintains structural consistency across all layers for improved face inpainting.

Findings

Effective recovery of heavily damaged face images

Outperforms existing methods in extreme inpainting scenarios

Maintains structural integrity in reconstructed faces

Abstract

Recovering badly damaged face images is a useful yet challenging task, especially in extreme cases where the masked or damaged region is very large. One of the major challenges is the ability of the system to generalize on faces outside the training dataset. We propose to tackle this extreme inpainting task with a conditional Generative Adversarial Network (GAN) that utilizes structural information, such as edges, as a prior condition. Edge information can be obtained from the partially masked image and a structurally similar image or a hand drawing. In our proposed conditional GAN, we pass the conditional input in every layer of the encoder while maintaining consistency in the distributions between the learned weights and the incoming conditional input. We demonstrate the effectiveness of our method with badly damaged face examples.