Automatic Generation of Commercial Scenes

Shaokui Zhang, Jia-Hong Liu, Yike Li, Tianyi Xiong, Ke-Xin Ren, Hongbo Fu, Song-Hai Zhang

Abstract: Commercial scenes such as markets and shops are everyday scenes for both virtual scenes and real-world interior designs. However, existing literature on interior scene synthesis mainly focuses on formulating and optimizing residential scenes such as bedrooms, living rooms, etc. Existing literature typically presents a set of relations among objects. It recognizes each furniture object as the smallest unit while optimizing a residential room. However, object relations become less critical in commercial scenes since shelves are often placed next to each other so pre-calculated relations of objects are less needed. Instead, interior designers resort to evaluating how groups of objects perform in commercial scenes, i.e., the smallest unit to be evaluated is a group of objects. This paper presents a system automatically synthesizes market-like commercial scenes in virtual environments. Following the rules of commercial layout design, we parameterize groups of objects as "patterns" contributing to a scene. Each pattern directly yields a human-centric routine locally, provides potential connectivity with other routines, and derives the arrangements of objects concerning itself according to the assigned parameters. In order to optimize a scene, the patterns are iteratively multiplexed to insert new routines or modify existing ones under a set of constraints derived from commercial layout designs. Through extensive experiments, we demonstrate the ability of our framework to generate plausible and practical commercial scenes.