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A recent Cell paper [Chang and Tsao, 2017] reports an interesting discovery.For the face stimuli generated by a pre-trained active appearance model (AAM),the responses of neurons in the areas of the primate brain that are responsiblefor face recognition exhibit strong linear relationship with the shapevariables and appearance variables of the AAM that generates the face stimuli.In this paper, we show that this behavior can be replicated by a deepgenerative model called the generator network, which assumes that the observedsignals are generated by latent random variables via a top-down convolutionalneural network. Specifically, we learn the generator network from the faceimages generated by a pre-trained AAM model using variational auto-encoder, andwe show that the inferred latent variables of the learned generator networkhave strong linear relationship with the shape and appearance variables of theAAM model that generates the face images. Unlike the AAM model that has anexplicit shape model where the shape variables generate the control points orlandmarks, the generator network has no such shape model and shape variables.Yet the generator network can learn the shape knowledge in the sense that someof the latent variables of the learned generator network capture the shapevariations in the face images generated by AAM.

Replicating Active Appearance Model by Generator Network