  In this work we combine two research threads from Vision/ Graphics and
Natural Language Processing to formulate an image generation task conditioned
on attributes in a multi-turn setting. By multiturn, we mean the image is
generated in a series of steps of user-specified conditioning information. Our
proposed approach is practically useful and offers insights into neural
interpretability. We introduce a framework that includes a novel training
algorithm as well as model improvements built for the multi-turn setting. We
demonstrate that this framework generates a sequence of images that match the
given conditioning information and that this task is useful for more detailed
benchmarking and analysis of conditional image generation methods.
