High-Accuracy Facial Depth Models derived from 3D Synthetic Data

26 Mar 2020 Khan Faisal Basak Shubhajit Javidnia Hossein Schukat Michael Corcoran Peter

In this paper, we explore how synthetically generated 3D face models can be used to construct a high accuracy ground truth for depth. This allows us to train the Convolutional Neural Networks (CNN) to solve facial depth estimation problems... (read more)

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