  Recent studies show increasing demands and interests in automatically
generating layouts, while there is still much room for improving the
plausibility and robustness. In this paper, we present a data-driven layout
framework without model formulation and loss term optimization. We achieve and
organize priors directly based on samples from datasets instead of sampling
probabilistic models. Therefore, our method enables expressing and generating
mathematically inexpressible relations among three or more objects.
Subsequently, a non-learning geometric algorithm attempts arranging objects
plausibly considering constraints such as walls, windows, etc. Experiments
would show our generated layouts outperform the state-of-art and our framework
is competitive to human designers.
