Function2Scene: 3D Indoor Scene Layout from Functional Specifications

Hugging Face Daily Papers Papers

Summary

Function2Scene generates 3D indoor layouts from functional descriptions by parsing user needs and applying design constraints through an iterative refinement loop combining geometric analysis, LLM reasoning, and VLM assessment, outperforming baselines in satisfying functional requirements.

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.
Original Article
View Cached Full Text

Cached at: 06/01/26, 03:18 AM

Paper page - Function2Scene: 3D Indoor Scene Layout from Functional Specifications

Source: https://huggingface.co/papers/2605.30819

Abstract

Function2Scene generates 3D indoor layouts from functional descriptions by parsing user needs and applying design constraints through an iterative refinement process combining geometric analysis, language modeling, and visual assessment.

Mosttext-driven 3D indoor scene synthesismethods 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., theiractivitiesand physical needs. We introduce Function2Scene, a framework for generating 3D indoor layouts fromfunctional specifications, i.e.,natural-language design briefsdescribing who will use a room and what they need to do there. Given such a specification, our system parsesoccupant personasandactivities, derives a customized set offunctional design constraintsfrom ataxonomy of 17 criteriaspanning 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-augmentedcheck-and-repair loop, combininggeometric measurements,LLM-based contextual reasoning, andVLM-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.

View arXiv pageView PDFProject pageAdd to collection

Get this paper in your agent:

hf papers read 2605\.30819

Don’t have the latest CLI?curl \-LsSf https://hf\.co/cli/install\.sh \| bash

Models citing this paper0

No model linking this paper

Cite arxiv.org/abs/2605.30819 in a model README.md to link it from this page.

Datasets citing this paper0

No dataset linking this paper

Cite arxiv.org/abs/2605.30819 in a dataset README.md to link it from this page.

Spaces citing this paper0

No Space linking this paper

Cite arxiv.org/abs/2605.30819 in a Space README.md to link it from this page.

Collections including this paper0

No Collection including this paper

Add this paper to acollectionto link it from this page.

Similar Articles

I Am Not a Reverse Centaur

Hacker News Top

Miguel Grinberg explains his refusal to accept unsolicited pull requests generated by LLMs, insisting on human involvement to avoid becoming a 'reverse centaur'.

SecureLens - A self-hosted AppSec agent and CLI scanner

Reddit r/AI_Agents

SecureLens is an open-source, self-hosted security auditing tool that uses an LLM-powered async pipeline to triage codebases and probe web infrastructure, with an interactive REPL for follow-up questions and patch generation.