Geometry-Based Layout Generation with Hyper-Relations AMONG Objects

Shao-Kui Zhang, Wei-Yu Xie, Song-Hai Zhang

Abstract: Recent studies show increasing demands and interests in automatic layout generation, while there is still much room for improving the plausibility and robustness. In this paper, we present a data-driven layout generation framework without model formulation and loss term optimization. We achieve and organize priors directly based on samples from datasets instead of sampling probabilistic distributions. Therefore, our method enables expressing relations among three or more objects that are hard to be mathematically modeled. Subsequently, a non-learning geometric algorithm is proposed to arrange objects considering constraints such as positions of walls and windows. Experiments show that the proposed method outperforms the state-of-the-art and our generated layouts are competitive to those designed by professionals.1