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Summary: the paper proposes a CNN for learning explicit image representations as an inverse graphics problem. The image representation has interpretable explicit representations, in particular pose angles and lighting angles, along with implicit representations (texture, appearance). This is done in an autoencoder framework with reconstruction error. To make a particular latent dimension focus on one aspect (e.g. Experiments on two datasets showing reconstructions of a 3D object at varying poses and illumination directions.