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[2602.18943] High Dimensional Procedural Content Generation
Summary
The paper introduces High-Dimensional Procedural Content Generation (HDPCG), a framework that enhances gameplay mechanics by treating non-geometric dimensions as integral to content generation, improving controllability and expressivity in game design.
Why It Matters
This research addresses limitations in traditional procedural content generation methods by proposing a framework that incorporates gameplay dimensions beyond geometry. This shift could lead to more versatile and engaging game design, impacting both developers and players in the gaming industry.
Key Takeaways
- HDPCG framework elevates non-geometric gameplay dimensions.
- Introduces two directions: Direction-Space and Direction-Time.
- Presents algorithms for abstract skeleton generation and validation.
- Validates methods through large-scale experiments and Unity case studies.
- Encourages a shift towards general representations in procedural content generation.
Computer Science > Artificial Intelligence arXiv:2602.18943 (cs) [Submitted on 21 Feb 2026] Title:High Dimensional Procedural Content Generation Authors:Kaijie Xu, Clark Verbrugge View a PDF of the paper titled High Dimensional Procedural Content Generation, by Kaijie Xu and 1 other authors View PDF HTML (experimental) Abstract:Procedural content generation (PCG) has made substantial progress in shaping static 2D/3D geometry, while most methods treat gameplay mechanics as auxiliary and optimize only over space. We argue that this limits controllability and expressivity, and formally introduce High-Dimensional PCG (HDPCG): a framework that elevates non-geometric gameplay dimensions to first-class coordinates of a joint state space. We instantiate HDPCG along two concrete directions. Direction-Space augments geometry with a discrete layer dimension and validates reachability in 4D (x,y,z,l), enabling unified treatment of 2.5D/3.5D mechanics such as gravity inversion and parallel-world switching. Direction-Time augments geometry with temporal dynamics via time-expanded graphs, capturing action semantics and conflict rules. For each direction, we present three general, practicable algorithms with a shared pipeline of abstract skeleton generation, controlled grounding, high-dimensional validation, and multi-metric evaluation. Large-scale experiments across diverse settings validate the integrity of our problem formulation and the effectiveness of our methods on playability, str...
