Game level layout from design specification

Abstract

The design of video game environments, or levels, aims to control gameplay by steering the player through a sequence of designer-controlled steps, while simultaneously providing a visually engaging experience. Traditionally these levels are painstakingly designed by hand, often from pre-existing building blocks, or space templates. In this paper, we propose an algorithmic approach for automatically laying out game levels from user-specified blocks. Our method allows designers to retain control of the gameplay flow via user-specified level connectivity graphs, while relieving them from the tedious task of manually assembling the building blocks into a valid, plausible layout. Our method produces sequences of diverse layouts for the same input connectivity, allowing for repeated replay of a given level within a visually different, new environment. We support complex graph connectivities and various building block shapes, and are able to compute complex layouts in seconds. The two key components of our algorithm are the use of configuration spaces defining feasible relative positions of building blocks within a layout and a graph-decomposition based layout strategy that leverages graph connectivity to speed up convergence and avoid local minima. Together these two tools quickly steer the solution toward feasible layouts. We demonstrate our method on a variety of real-life inputs, and generate appealing layouts conforming to user specifications.

Graph-based approach for enumerating floorplans based on users specifications

“…Also, along with adjacencies and dimensions, we have considered boundary and symmetric constraints, which help us to customize floorplans based on users requirements. At this stage, this work, in general, proposes floorplan construction, which has applications in housing design (Hu et al , 2020), game layout design (Ma et al , 2014), VLSI design (Kahng et al , 2011), etc. In future, we aim to customize this work for housing design where the user only needs to present his requirements in terms of spaces and GPLAN would automatically provide a set of layouts.…”

House-GAN++: Generative Adversarial Layout Refinement Networks

“…Integer programming is formulated to generate an arrangement of tiles [26]. Optimization has also proven effective for game-level design [20,12]. For floorplans specifically, Bayesian network is trained to learn distributions of architectural components for stochastic sample generation [22].…”

Metabopolis: scalable network layout for biological pathway diagrams in urban map style

“…For example, Merrell et al [39] developed an approach to automatically design room layouts trained on real-world data and Ma et al [40] calculated a room plan based on a planar graph specified by game designers. Both methods introduced configuration space techniques to further constrain object placement during the optimization process.…”

“…This is achieved by optimizing the block positions according to the connectivity of G C . Note that planar graphs have been previously used to guide users for designing a room layout [39, 40] guaranteeing a doorway continuity. However, our graph skeleton should not only serve this purpose, but should also present users with a clear information whether the designed graph will produce a solvable result.…”