Current methods for generating 3D scene layouts from text predominantly follow a declarative paradigm, where a Large Language Model (LLM) specifies high-level constraints that are then resolved by a separate solver. This paper challenges that consensus by introducing a more direct, imperative approach. We task an LLM with generating a step-by-step program that iteratively places each object relative to those already in the scene. This paradigm simplifies the underlying scene specification language, enabling the creation of more complex, varied, and highly structured layouts that are difficult to express declaratively. To improve the robustness, we complement our method with a novel, LLM-free error correction mechanism that operates directly on the generated code, iteratively adjusting parameters within the program to resolve collisions and other inconsistencies. In forced-choice perceptual studies, human participants overwhelmingly preferred our imperative layouts, choosing them over those from two state-of-the-art declarative systems 82% and 94% of the time, demonstrating the significant potential of this alternative paradigm. Finally, we present a simple automated evaluation metric for 3D scene layout generation that correlates strongly with human judgment.
@article{arxiv.2504.05482,
title = {Imperative vs. Declarative Programming Paradigms for Open-Universe Scene Generation},
author = {Maxim Gumin and Do Heon Han and Seung Jean Yoo and Aditya Ganeshan and R. Kenny Jones and Rio Aguina-Kang and Stewart Morris and Daniel Ritchie},
journal= {arXiv preprint arXiv:2504.05482},
year = {2025}
}