Geometry-Based Layout Generation with Hyper-Relations AMONG Objects.

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.