# Multi-Domain Level Generation and Blending with Sketches via Example-Driven BSP and Variational Autoencoders

arXiv: https://arxiv.org/abs/2006.09807 · HTML: https://mlsanity.com/paper/2006.09807/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jun 2020.

## Built on by (most cited first, 11 of 11)

- [Level Generation Through Large Language Models](https://mlsanity.com/paper/2302.05817/) (2023)
- [Generating and Blending Game Levels via Quality-Diversity in the Latent Space of a Variational Autoencoder](https://mlsanity.com/paper/2102.12463/) (2021)
- [Procedural Content Generation in Games: A Survey with Insights on Emerging LLM Integration](https://mlsanity.com/paper/2410.15644/) (2024)
- [Dungeon and Platformer Level Blending and Generation using Conditional VAEs](https://mlsanity.com/paper/2106.12692/) (2021)
- [Lode Encoder: AI-constrained co-creativity](https://mlsanity.com/paper/2308.01312/) (2023)
- [tile2tile: Learning Game Filters for Platformer Style Transfer](https://mlsanity.com/paper/2208.07699/) (2022)
- [Procedural Content Generation via Knowledge Transformation (PCG-KT)](https://mlsanity.com/paper/2305.00644/) (2023)
- [Reconstructing Existing Levels through Level Inpainting](https://mlsanity.com/paper/2309.09472/) (2023)
- [Latent Combinational Game Design](https://mlsanity.com/paper/2206.14203/) (2022)
- [Expanding Horizons of Level Diversity via Multi-objective Evolutionary Learning](https://mlsanity.com/paper/2509.24341/) (2025)
- [Learning Local Constraints for Reinforcement-Learned Content Generators](https://mlsanity.com/paper/2605.13570/) (2026)

## Abstract (arXiv)

Procedural content generation via machine learning (PCGML) has demonstrated its usefulness as a content and game creation approach, and has been shown to be able to support human creativity. An important facet of creativity is combinational creativity or the recombination, adaptation, and reuse of ideas and concepts between and across domains. In this paper, we present a PCGML approach for level generation that is able to recombine, adapt, and reuse structural patterns from several domains to approximate unseen domains. We extend prior work involving example-driven Binary Space Partitioning for recombining and reusing patterns in multiple domains, and incorporate Variational Autoencoders (VAEs) for generating unseen structures. We evaluate our approach by blending across 7 domains and subsets of those domains. We show that our approach is able to blend domains together while retaining structural components. Additionally, by using different groups of training domains our approach is able to generate both 1) levels that reproduce and capture features of a target domain, and 2) levels that have vastly different properties from the input domain.