FaceEraser: Removing Facial Parts for Augmented Reality

TL;DR

This paper introduces FaceEraser, a novel method for removing facial features to enable augmented reality applications, utilizing a new data generation technique and an improved inpainting network architecture.

Contribution

It presents a new data generation approach for training facial parts removal models and a novel network architecture for better inpainting quality.

Findings

Effective removal of facial parts demonstrated

Enhanced inpainting quality over existing methods

Various face-oriented AR applications showcased

Abstract

Our task is to remove all facial parts (e.g., eyebrows, eyes, mouth and nose), and then impose visual elements onto the ``blank'' face for augmented reality. Conventional object removal methods rely on image inpainting techniques (e.g., EdgeConnect, HiFill) that are trained in a self-supervised manner with randomly manipulated image pairs. Specifically, given a set of natural images, randomly masked images are used as inputs and the raw images are treated as ground truths. Whereas, this technique does not satisfy the requirements of facial parts removal, as it is hard to obtain ``ground-truth'' images with real ``blank'' faces. To address this issue, we propose a novel data generation technique to produce paired training data that well mimic the ``blank'' faces. In the mean time, we propose a novel network architecture for improved inpainting quality for our task. Finally, we demonstrate various face-oriented augmented reality applications on top of our facial parts removal model. The source codes are released at \href{https://github.com/duxingren14/FaceEraser}{duxingren14/FaceEraser} on github for research purposes.