Function2Scene: 3D Indoor Scene Layout from Functional Specifications
This work addresses the problem of generating functionally appropriate 3D indoor scenes for interior designers and users by shifting from object-centric to function-centric specifications, offering a significant improvement over existing methods.
This paper introduces Function2Scene, a framework that generates 3D indoor layouts from natural-language functional specifications describing room usage rather than object lists. It parses occupant personas and activities, derives 17 functional design constraints, and uses an iterative check-and-repair loop for layout generation. Function2Scene produced layouts that better satisfied functional requirements than LLM-based baselines, with a preference in 94.3% of pairwise comparisons across 30 design cases.
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.