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arXiv · 2006.11341

Real-time Pupil Tracking from Monocular Video for Digital Puppetry

Artsiom Ablavatski·Andrey Vakunov·Ivan Grishchenko·Karthik Raveendran·Matsvei Zhdanovich
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Abstract

We present a simple, real-time approach for pupil tracking from live video on mobile devices. Our method extends a state-of-the-art face mesh detector with two new components: a tiny neural network that predicts positions of the pupils in 2D, and a displacement-based estimation of the pupil blend shape coefficients. Our technique can be used to accurately control the pupil movements of a virtual puppet, and lends liveliness and energy to it. The proposed approach runs at over 50 FPS on modern phones, and enables its usage in any real-time puppeteering pipeline.

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Artsiom Ablavatski, Andrey Vakunov, Ivan Grishchenko, Karthik Raveendran, Matsvei Zhdanovich. 2020-06-19. Real-time Pupil Tracking from Monocular Video for Digital Puppetry. https://arxiv.org/abs/2006.11341

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