  This paper proposes a novel generative adversarial layout refinement network
for automated floorplan generation. Our architecture is an integration of a
graph-constrained relational GAN and a conditional GAN, where a previously
generated layout becomes the next input constraint, enabling iterative
refinement. A surprising discovery of our research is that a simple
non-iterative training process, dubbed component-wise GT-conditioning, is
effective in learning such a generator. The iterative generator also creates a
new opportunity in further improving a metric of choice via meta-optimization
techniques by controlling when to pass which input constraints during iterative
layout refinement. Our qualitative and quantitative evaluation based on the
three standard metrics demonstrate that the proposed system makes significant
improvements over the current state-of-the-art, even competitive against the
ground-truth floorplans, designed by professional architects.
