  Text conditioned generative models for images have yielded impressive
results. Text conditioned floorplan generation as a special type of raster
image generation task also received particular attention. However there are
many use cases in floorpla generation where numerical properties of the
generated result are more important than the aesthetics. For instance, one
might want to specify sizes for certain rooms in a floorplan and compare the
generated floorplan with given specifications Current approaches, datasets and
commonly used evaluations do not support these kinds of constraints. As such,
an attractive strategy is to generate an intermediate data structure that
contains numerical properties of a floorplan which can be used to generate the
final floorplan image. To explore this setting we (1) construct a new dataset
for this data-structure to data-structure formulation of floorplan generation
using two popular image based floorplan datasets RPLAN and ProcTHOR-10k, and
provide the tools to convert further procedurally generated ProcTHOR floorplan
data into our format. (2) We explore the task of floorplan generation given a
partial or complete set of constraints and we design a series of metrics and
benchmarks to enable evaluating how well samples generated from models respect
the constraints. (3) We create multiple baselines by finetuning a large
language model (LLM), Llama3, and demonstrate the feasibility of using
floorplan data structure conditioned LLMs for the problem of floorplan
generation respecting numerical constraints. We hope that our new datasets and
benchmarks will encourage further research on different ways to improve the
performance of LLMs and other generative modelling techniques for generating
designs where quantitative constraints are only partially specified, but must
be respected.
