paper-with-me

Papers

PlotMap: Automated Layout Design for Building Game Worlds

2023-09-26 · Yi Wang, Jieliang Luo, Adam Gaier, Evan Atherton, Hilmar Koch

World-building, the process of developing both the narrative and physical world of a game, plays a vital role in the game's experience. Critically-acclaimed independent and AAA video games are praised for strong world-building, with game maps that masterfully intertwine with and elevate the narrative, captivating players and leaving a lasting impression. However, designing game maps that support a desired narrative is challenging, as it requires satisfying complex constraints from various considerations. Most existing map generation methods focus on considerations about gameplay mechanics or map topography, while the need to support the story is typically neglected. As a result, extensive manual adjustment is still required to design a game world that facilitates particular stories. In this work, we approach this problem by introducing an extra layer of plot facility layout design that is independent of the underlying map generation method in a world-building pipeline. Concretely, we define (plot) facility layout tasks as the tasks of assigning concrete locations on a game map to abstract locations mentioned in a given story (plot facilities), following spatial constraints derived from the story. We present two methods for solving these tasks automatically: an evolutionary computation based approach through Covariance Matrix Adaptation Evolution Strategy (CMA-ES), and a Reinforcement Learning (RL) based approach. We develop a method of generating datasets of facility layout tasks, create a gym-like environment for experimenting with and evaluating different methods, and further analyze the two methods with comprehensive experiments, aiming to provide insights for solving facility layout tasks. We will release the code and a dataset containing 10, 000 tasks of different scales.

📄 PDF Abstract BibTeX arXiv:2309.15242

Code (1)

autodeskailab/plotmap 공식 구현 pytorch

Tasks

Decision MakingLayout DesignReinforcement Learning (RL)

Methods 이 논문이 사용한 방법론

Focus 설명 없음

Similar Papers 제목 키워드 기반

RoBus: A Multimodal Dataset for Controllable Road Networks and Building Layouts Generation

2024-07-10 · Tao Li, Ruihang Li, Huangnan Zheng, Shanding Ye 외

Automated 3D city generation, focusing on road networks and building layouts, is in high demand for applications in urban design, multimedia games and autonomous driving simulations. The surge of generative AI facilitate…

Autonomous Driving

Towards Automatic Design of Factorio Blueprints

2023-10-02 · Sean Patterson, Joan Espasa, Mun See Chang, Ruth Hoffmann

Factorio is a 2D construction and management simulation video game about building automated factories to produce items of increasing complexity. A core feature of the game is its blueprint system, which allows players to…

Management

Offline Grid-Based Coverage path planning for guards in games

2020-01-15 · Wael Al Enezi, Clark Verbrugge

Algorithmic approaches to exhaustive coverage have application in video games, enabling automatic game level exploration. Current designs use simple heuristics that frequently result in poor performance or exhibit unnatu…

Graph2Plan: Learning Floorplan Generation from Layout Graphs

2020-04-27 · Ruizhen Hu, Zeyu Huang, Yuhan Tang, Oliver van Kaick 외

We introduce a learning framework for automated floorplan generation which combines generative modeling using deep neural networks and user-in-the-loop designs to enable human users to provide sparse design constraints. …

Graph Neural Network

Sketch2Scene: Automatic Generation of Interactive 3D Game Scenes from User's Casual Sketches

2024-08-08 · Yongzhi Xu, Yonhon Ng, Yifu Wang, Inkyu Sa 외

3D Content Generation is at the heart of many computer graphics applications, including video gaming, film-making, virtual and augmented reality, etc. This paper proposes a novel deep-learning based approach for automati…

DenoisingUnity