FaceEraser: Removing Facial Parts for Augmented Reality
Miao Hua, Lijie Liu, Ziyang Cheng, Qian He, Bingchuan Li, Zili Yi

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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsFace recognition and analysis · Generative Adversarial Networks and Image Synthesis · Advanced Image Processing Techniques
MethodsInpainting
