Ivan Grishchenko
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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. This solution enables applications like AR makeup, eye tracking and 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.
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Real-time Facial Surface Geometry from Monocular Video on Mobile GPUs
Artsiom Ablavatski
CVPR Workshop on Computer Vision for Augmented and Virtual Reality 2019, IEEE, Long Beach, CA
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We present an end-to-end neural network-based model for inferring the approximate 3D mesh representation of a human face from single camera input for AR applications. The relatively dense mesh model of 468 vertices is well-suited for face-based AR effects. The proposed model demonstrates super-realtime inference speed on mobile GPUs (100--1000+ FPS, depending on the device and model variant) and a high prediction quality that is comparable to the variance in manual annotations of the same image.
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