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We present an approach to generate high fidelity 3D face avatar with ahigh-resolution UV texture map from a single image. To estimate the facegeometry, we use a deep neural network to directly predict vertex coordinatesof the 3D face model from the given image. The 3D face geometry is furtherrefined by a non-rigid deformation process to more accurately capture faciallandmarks before texture projection. A key novelty of our approach is to trainthe shape regression network on facial images synthetically generated using ahigh-quality rendering engine. Moreover, our shape estimator fully leveragesthe discriminative power of deep facial identity features learned from millionsof facial images. We have conducted extensive experiments to demonstrate thesuperiority of our optimized 2D-to-3D rendering approach, especially itsexcellent generalization property on real-world selfie images. Our proposedsystem of rendering 3D avatars from 2D images has a wide range of applicationsfrom virtual/augmented reality (VR/AR) and telepsychiatry to human-computerinteraction and social networks.

Digital Twin: Acquiring High-Fidelity 3D Avatar from a Single Image