# Function2Scene: 3D Indoor Scene Layout from Functional Specifications

Page: https://stenobird.com/podcast/daily-paper-cast-7079649/function2scene-3d-indoor-scene-layout-from-functional-specifications
Text version: https://stenobird.com/podcast/daily-paper-cast-7079649/function2scene-3d-indoor-scene-layout-from-functional-specifications.md
Podcast: [Daily Paper Cast](https://stenobird.com/podcast/daily-paper-cast-7079649)
Published: 2026-06-02T04:13:49+00:00
Episode link: https://share.transistor.fm/s/7837f3e1
Audio file: https://media.transistor.fm/7837f3e1/c9765ad6.mp3
Processing state: not_requested
JSON: https://stenobird.com/v1/public/podcasts/daily-paper-cast-7079649/episodes/function2scene-3d-indoor-scene-layout-from-functional-specifications
Duration seconds: 1318

## Resource

🤗 Upvotes: 33 | cs.CV Authors: Ruiqi Wang, Qimin Chen, Daniel Ritchie, Angel X. Chang, Manolis Savva, Kai Wang, Hao Zhang Title: Function2Scene: 3D Indoor Scene Layout from Functional Specifications Arxiv: http://arxiv.org/abs/2605.30819v1 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.

## Actions

- request_transcript: `POST https://stenobird.com/v1/public/podcasts/daily-paper-cast-7079649/episodes/function2scene-3d-indoor-scene-layout-from-functional-specifications/transcription-requests` — Idempotently request low-priority transcript generation for this episode.
- read_markdown: `GET https://stenobird.com/podcast/daily-paper-cast-7079649/function2scene-3d-indoor-scene-layout-from-functional-specifications.md` — Read the agent-friendly Markdown representation of this episode resource.

A page view does not enqueue transcription. Agents should invoke `request_transcript` explicitly when they need this episode processed.

## Transcript

Full transcripts are not published on public pages unless there is a clear rights basis.