  We address the problem of indoor layout synthesis, which is a topic of
continuing research interest in computer graphics. The newest works made
significant progress using data-driven generative methods; however, these
approaches rely on suitable datasets. In practice, desirable layout properties
may not exist in a dataset, for instance, specific expert knowledge can be
missing in the data. We propose a method that combines expert knowledge, for
example, knowledge about ergonomics, with a data-driven generator based on the
popular Transformer architecture. The knowledge is given as differentiable
scalar functions, which can be used both as weights or as additional terms in
the loss function. Using this knowledge, the synthesized layouts can be biased
to exhibit desirable properties, even if these properties are not present in
the dataset. Our approach can also alleviate problems of lack of data and
imperfections in the data. Our work aims to improve generative machine learning
for modeling and provide novel tools for designers and amateurs for the problem
of interior layout creation.
