  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.
