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