We perform a systematic analysis in order to determine how the presence of different types of variability in the training data affects the generalization properties of the network for 3-dimensional head reconstruction.
Convolutional neural networks have recently shown great success in computer vision. They are able to automatically learn complicated mappings, often reaching human or super-human performance. However, a lack of labeled data can preclude the training of such networks. This is the case in the reconstruction of 3-dimensional human heads from 2-dimensional photographs. Approaching the problem backwards, starting from 3-dimensional heads and using photo-realistic rendering, one can create any number of training data to tackle the problem. This way, fine control over the data allows for new insights into how a convolutional neural network interprets data and how variability in the training and test data affect its performance. We perform a systematic analysis in order to determine how the presence of different types of variability in the training data affects the generalization properties of the network for 3-dimensional head reconstruction.