Decorum: A Language-Based Approach For Style-Conditioned Synthesis of Indoor 3D Scenes

3D indoor scene generation is an important problem for the design of digital and real-world environments. To automate this process, a scene generation model should be able to not only generate plausible scene layouts, but also take into consideration visual features and style preferences. Existing methods for this task exhibit very limited control over these attributes, only allowing text inputs in the form of simple object-level descriptions or pairwise spatial relationships. Our proposed method Decorum enables users to control the scene generation process with natural language by adopting language-based representations at each stage. This enables us to harness recent advancements in Large Language Models (LLMs) to model language-to-language mappings. In addition, we show that using a text-based representation allows us to select furniture for our scenes using a novel object retrieval method based on multimodal LLMs. Evaluations on the benchmark 3D-FRONT dataset show that our methods achieve improvements over existing work in text-conditioned scene synthesis and object retrieval.

@article{marshall2025_2503.18155,
  title={ Decorum: A Language-Based Approach For Style-Conditioned Synthesis of Indoor 3D Scenes },
  author={ Kelly O. Marshall and Omid Poursaeed and Sergiu Oprea and Amit Kumar and Anushrut Jignasu and Chinmay Hegde and Yilei Li and Rakesh Ranjan },
  journal={arXiv preprint arXiv:2503.18155},
  year={ 2025 }
}