  This paper investigates a novel problem of generating images from visual
attributes. We model the image as a composite of foreground and background and
develop a layered generative model with disentangled latent variables that can
be learned end-to-end using a variational auto-encoder. We experiment with
natural images of faces and birds and demonstrate that the proposed models are
capable of generating realistic and diverse samples with disentangled latent
representations. We use a general energy minimization algorithm for posterior
inference of latent variables given novel images. Therefore, the learned
generative models show excellent quantitative and visual results in the tasks
of attribute-conditioned image reconstruction and completion.
