  We present Attention Mesh, a lightweight architecture for 3D face mesh
prediction that uses attention to semantically meaningful regions. Our neural
network is designed for real-time on-device inference and runs at over 50 FPS
on a Pixel 2 phone. Our solution enables applications like AR makeup, eye
tracking and AR puppeteering that rely on highly accurate landmarks for eye and
lips regions. Our main contribution is a unified network architecture that
achieves the same accuracy on facial landmarks as a multi-stage cascaded
approach, while being 30 percent faster.
