Keiki: Towards Realistic Danmaku Generation via Sequential GANs
This addresses the challenge of realistic content generation for game developers, though it appears incremental as it builds on existing GAN methods for a specific domain.
The paper tackles the problem of generating realistic danmaku patterns in bullet hell games by introducing Keiki, a platform that models danmakus as parametric sequences, and finds that time-series and periodic spatial GANs achieve competitive performance in metrics like deviation from human designs and diversity.
Search-based procedural content generation methods have recently been introduced for the autonomous creation of bullet hell games. Search-based methods, however, can hardly model patterns of danmakus -- the bullet hell shooting entity -- explicitly and the resulting levels often look non-realistic. In this paper, we present a novel bullet hell game platform named Keiki, which allows the representation of danmakus as a parametric sequence which, in turn, can model the sequential behaviours of danmakus. We employ three types of generative adversarial networks (GANs) and test Keiki across three metrics designed to quantify the quality of the generated danmakus. The time-series GAN and periodic spatial GAN show different yet competitive performance in terms of the evaluation metrics adopted, their deviation from human-designed danmakus, and the diversity of generated danmakus. The preliminary experimental studies presented here showcase that potential of time-series GANs for sequential content generation in games.