  We present a generative model of images based on layering, in which image
layers are individually generated, then composited from front to back. We are
thus able to factor the appearance of an image into the appearance of
individual objects within the image --- and additionally for each individual
object, we can factor content from pose. Unlike prior work on layered models,
we learn a shape prior for each object/layer, allowing the model to tease out
which object is in front by looking for a consistent shape, without needing
access to motion cues or any labeled data. We show that ordinary stochastic
gradient variational bayes (SGVB), which optimizes our fully differentiable
lower-bound on the log-likelihood, is sufficient to learn an interpretable
representation of images. Finally we present experiments demonstrating the
effectiveness of the model for inferring foreground and background objects in
images.
