Paper Detail
Fedor Rodionov, Aleksandar Cvejic, Michael Birsak, John Femiani, Peter Wonka
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.
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@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},
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 da},
url = {https://huggingface.co/papers/2606.10953},
keywords = {vision-language model, domain-specific language, procedural reasoning, preference optimization, semantic masks, Flux-based LoRA renderer, huggingface daily},
eprint = {2606.10953},
archiveprefix = {arXiv},
}
{}