Paper ID: 2309.05782

Blendshapes GHUM: Real-time Monocular Facial Blendshape Prediction

Ivan Grishchenko, Geng Yan, Eduard Gabriel Bazavan, Andrei Zanfir, Nikolai Chinaev, Karthik Raveendran, Matthias Grundmann, Cristian Sminchisescu

We present Blendshapes GHUM, an on-device ML pipeline that predicts 52 facial blendshape coefficients at 30+ FPS on modern mobile phones, from a single monocular RGB image and enables facial motion capture applications like virtual avatars. Our main contributions are: i) an annotation-free offline method for obtaining blendshape coefficients from real-world human scans, ii) a lightweight real-time model that predicts blendshape coefficients based on facial landmarks.

Submitted: Sep 11, 2023