  Layout design is ubiquitous in many applications, e.g. architecture/urban
planning, etc, which involves a lengthy iterative design process. Recently,
deep learning has been leveraged to automatically generate layouts via image
generation, showing a huge potential to free designers from laborious routines.
While automatic generation can greatly boost productivity, designer input is
undoubtedly crucial. An ideal AI-aided design tool should automate repetitive
routines, and meanwhile accept human guidance and provide smart/proactive
suggestions. However, the capability of involving humans into the loop has been
largely ignored in existing methods which are mostly end-to-end approaches. To
this end, we propose a new human-in-the-loop generative model, iPLAN, which is
capable of automatically generating layouts, but also interacting with
designers throughout the whole procedure, enabling humans and AI to co-evolve a
sketchy idea gradually into the final design. iPLAN is evaluated on diverse
datasets and compared with existing methods. The results show that iPLAN has
high fidelity in producing similar layouts to those from human designers, great
flexibility in accepting designer inputs and providing design suggestions
accordingly, and strong generalizability when facing unseen design tasks and
limited training data.
