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3D Morphable Models (3DMMs) are statistical models that represent facialtexture and shape variations using a set of linear bases and more particularPrincipal Component Analysis (PCA). 3DMMs were used as statistical priors forreconstructing 3D faces from images by solving non-linear least squareoptimization problems. Recently, 3DMMs were used as generative models fortraining non-linear mappings (\ie, regressors) from image to the parameters ofthe models via Deep Convolutional Neural Networks (DCNNs). Nevertheless, all ofthe above methods use either fully connected layers or 2D convolutions onparametric unwrapped UV spaces leading to large networks with many parameters.In this paper, we present the first, to the best of our knowledge, non-linear3DMMs by learning joint texture and shape auto-encoders using direct meshconvolutions. We demonstrate how these auto-encoders can be used to train verylight-weight models that perform Coloured Mesh Decoding (CMD) in-the-wild at aspeed of over 2500 FPS.

Dense 3D Face Decoding over 2500FPS: Joint Texture & Shape Convolutional Mesh Decoders