| title | Architect-Ant: Editable Automatic Furnishing of Architectural Floor Plans |
|---|---|
| authors | Fedor Rodionov, Aleksandar Cvejic, Michael Birsak, John Femiani, Peter Wonka |
| date | 2026-09-30 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2606.10953 |
| affiliation | KAUST Center of Excellence in Generative AI |
| upvotes | 29 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf |
| abstract | Furnished floor plans support real-estate visualization, interior design, and architectural workflows, yet automatic furnishing remains challenged by limited real-world data and the need to satisfy interacting geometric and functional constraints. We ask whether professional furnishing knowledge can be learned from real floor plans using a pretrained model, enabling direct constraint-aware layout generation without relying on costly iterative agentic inference. We introduce AntPlan, a curated dataset of 505 real professional architectural floor plans with dense furniture annotations spanning 92 object classes and ten residential room categories, and Architect-Ant, a framework for generating furniture layouts. Architect-Ant represents layouts with an editable coordinate-based DSL and first learns professional furnishing patterns through supervised fine-tuning. It is then optimized with GRPO using a Layout Rule Score (LRS) that aggregates geometric and functional constraints derived from professional plans, providing outcome-level supervision without prescribed reasoning traces. Experiments against diverse state-of-the-art baselines show that Architect-Ant combines low geometric violation rates with high functional completeness, while qualitative results more closely reflect real-world residential furnishing patterns. The resulting layouts remain object-level editable and can be converted into 3D scenes. |
cite
@misc{rodionov2026architect,
title={Architect-Ant: Editable Automatic Furnishing of Architectural Floor Plans},
author={Fedor Rodionov and Aleksandar Cvejic and Michael Birsak and John Femiani and Peter Wonka},
year={2026},
eprint={2606.10953},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2606.10953}
}