| title | Function2Scene: 3D Indoor Scene Layout from Functional Specifications |
|---|---|
| authors | Ruiqi Wang, Qimin Chen, Daniel Ritchie, Angel X. Chang, Manolis Savva, Kai Wang, Hao Zhang |
| date | 2026-05-29 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2605.30819 |
| upvotes | 38 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf |
| abstract | Most text-driven 3D indoor scene synthesis methods generate rooms from object-centric prompts, asking what furniture should be placed rather than how the space is used. Yet in real interior design, a layout is judged by how well it supports its occupants, e.g., their activities and physical needs. We introduce Function2Scene, a framework for generating 3D indoor layouts from functional specifications, i.e., natural-language design briefs describing who will use a room and what they need to do there. Given such a specification, our system parses occupant personas and activities, derives a customized set of functional design constraints from a taxonomy of 17 criteria spanning spatial, ergonomic, activity, and environmental considerations, and uses these constraints to guide layout generation. Rather than relying on an LLM to directly produce a final scene, Function2Scene performs iterative evaluation and refinement through a tool-augmented check-and-repair loop, combining geometric measurements, LLM-based contextual reasoning, and VLM-based visual assessment. Experiments on 30 professionally written interior-design cases show that Function2Scene produces layouts that better satisfy functional requirements than recent LLM-based scene synthesis baselines, with our results preferred in 94.3% of pairwise comparisons. Our work reframes text-driven indoor scene synthesis from placing plausible objects to designing spaces that support human use. |
cite
@misc{wang2026function,
title={Function2Scene: 3D Indoor Scene Layout from Functional Specifications},
author={Ruiqi Wang and Qimin Chen and Daniel Ritchie and Angel X. Chang and Manolis Savva and Kai Wang and Hao Zhang},
year={2026},
eprint={2605.30819},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2605.30819}
}