We introduce an alternative method for open-world scene generation. In this thesis, Graph-based Wave Function Collapse (GWFC) is integrated with Space Colonization Algorithm (SCA) and used to place objects in an unstructured 3D environment. This combined algorithm, Space Colonization Graph-based Wave Function Collapse (SC-GWFC), leverages the constraint-based capabilities of GWFC and the ability of SCA to populate arbitrary 3D volumes. We demonstrate that objects of variable scale can be successfully used with SC-GWFC. Since this algorithm is run in an interactive environment, we demonstrate iterative modifications to a partially complete scene and incorporate PCG into a scene editing process. As part of the implementation, we also introduce our Scene Modeling Application for rendering and editing 3D scenes. This modeling application allows for editing and viewing constraints for our SC-GWFC scene generator. We evaluate the performance characteristics of SC-GWFC in the Scene Modeling Application to demonstrate that SC-GWFC can be used interactively. Through the application, users can specify adjacency requirements for objects, and SC-GWFC will attempt to place objects in patterns that respect these rules. We demonstrate the ability to place up to 5000 items on a terrain using our proposed SC-GWFC technique.

When a production company commits to creating a film based on a novel, it is essential that their team is equipped to manage the extensive responsibilities required to authentically translate the book to the big screen. This study aims to explore and address these challenges by utilizing contemporary Generative Artificial Intelligence technologies, including Large Language Models, Text-To-Speech, and Text-To-Image models. While recent advancements have focused on enhancing these models, there is a gap in research on their practical application and effectiveness in real-world scenarios. This research will detail the steps necessary to deconstruct a novel’s narrative and produce the final cinematic product. Additionally, it will propose novel methods to mitigate errors such as hallucinations generated by Language Models and image models, enhancing the fidelity and quality of the adaptations. iii