A Novel Face Inpainting Approach Based on Guided Deep Learning

摘要

In the last few years, deep learning has shown significant improvement for many computer vision open problems, especially Image inpainting. Image inpainting is the process of filling missing regions across images. One of the most challenging problems in image inpainting is face inpainting. In this work, a new novel approach for face inpainting is proposed which can capture and preserve the identity of each human face in images while reproducing the missing irregular region in images. A two-stage cascaded model is proposed. It is composed of a shape-predictor of the key-points of the face followed by an inpainting network. The shape-predictor identifies the human face's structure-preserving its local points, i.e. eyes, mouth, nose, and then the inpainting network fills any random-irregular missing regions guided by the obtained knowledge as a priori. The effectiveness of the proposed model was evaluated using the CelebA dataset. The obtained results from the trained model outperform the recently proposed technique with contextual attention.