Unsupervised Training for 3D Morphable Model Regression

CVPR 2018 Kyle GenovaForrester ColeAaron MaschinotAaron SarnaDaniel VlasicWilliam T. Freeman

We present a method for training a regression network from image pixels to 3D morphable model coordinates using only unlabeled photographs. The training loss is based on features from a facial recognition network, computed on-the-fly by rendering the predicted faces with a differentiable renderer... (read more)

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Evaluation results from the paper

#2 best model for 3D Face Reconstruction on Florence (Average 3D Error metric)

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Task Dataset Model Metric name Metric value Global rank Compare
3D Face Reconstruction Florence Unsupervised-3DMMR Average 3D Error 1.50 # 2