  We present a recurrent agent who perceives surroundings through a series of
discrete fixations. At each timestep, the agent imagines a variety of plausible
scenes consistent with the fixation history. The next fixation is planned using
uncertainty in the content of the imagined scenes. As time progresses, the
agent becomes more certain about the content of the surrounding, and the
variety in the imagined scenes reduces. The agent is built using a variational
autoencoder and normalizing flows, and trained in an unsupervised manner on a
proxy task of scene-reconstruction. The latent representations of the imagined
scenes are found to be useful for performing pixel-level and scene-level tasks
by higher-order modules. The agent is tested on various 2D and 3D datasets.
