Self-Supervised Learning for Procedural Content Generation with Temporal Consistency

Abstract

Procedural Content Generation (PCG) has long been a challenging area within computer graphics and game development, primarily due to the difficulty in achieving temporal consistency and coherence in generated content. This work introduces a novel approach leveraging self-supervised learning to address this limitation. The system learns to generate sequences of content-such as level designs, animations, or musical pieces-by minimizing a contrastive loss that encourages consistency between successive samples. This results in a temporal representation of the desired narrative, leading to more believable and engaging generated experiences. The core idea is to treat content generation as a learning problem, allowing the system to implicitly capture the stylistic and narrative constraints inherent in the desired output. The system utilizes a latent space learned through self-supervised training to guide content generation, ensuring a degree of control and allowing for exploration of diverse yet consistent outputs. This research demonstrates a pathway towards PCG systems that can produce temporally consistent and coherent content, moving beyond purely random or rule-based generation methods.