  Conditional text-to-image generation is an active area of research, with many
possible applications. Existing research has primarily focused on generating a
single image from available conditioning information in one step. One practical
extension beyond one-step generation is a system that generates an image
iteratively, conditioned on ongoing linguistic input or feedback. This is
significantly more challenging than one-step generation tasks, as such a system
must understand the contents of its generated images with respect to the
feedback history, the current feedback, as well as the interactions among
concepts present in the feedback history. In this work, we present a recurrent
image generation model which takes into account both the generated output up to
the current step as well as all past instructions for generation. We show that
our model is able to generate the background, add new objects, and apply simple
transformations to existing objects. We believe our approach is an important
step toward interactive generation. Code and data is available at:
https://www.microsoft.com/en-us/research/project/generative-neural-visual-artist-geneva/ .
