RenoLayout is a new framework that unifies floorplan renovation and furniture layout for residential design. It takes an existing residential layout and a set of design points, then generates a Geometry-as-Code program that preserves immutable structural elements while optimizing furniture placement under design objectives and physical constraints. The authors also introduce a Design Map with 123 fine‑grained design points, a Difference‑Grounded Design‑Point Annotation method, and a three‑stage training pipeline (CPT–SFT–RL) to improve spatial reasoning, instruction following, and constraint compliance. Additionally, they present the RenoLayout Benchmark, a collection of 200 real‑world before‑after renovation cases with complete furniture layouts, marking the first benchmark for unified residential design.
arXiv:2606. 10953v1 Announce Type: new Abstract: Furnished floor plans are fundamental to real estate visualization, interior design, and architectural workflows.
PlanCraft introduces a progressive approach to 3D residential scene generation that mirrors how architects design: starting with rough sketches and refining them over time. It leverages a large dataset of real floor plans to train a SketchPlan module that generates partial sketches at various completion levels, a PlanCraft‑Diff module that sharpens these sketches into precise vector floor plans, and a PlanCraft‑Agent that furnishes rooms within the established spatial contract. The method outperforms existing 2D and 3D baselines, achieving a 61.1% lower FID and a 15‑point lead in expert‑rated spatial rationality, even with only 25% sketch completion.
arXiv:2608.29519v1 Announce Type: new Abstract: We introduce Function-Room Generation, a new indoor 3D scene generation setting that creates rooms supporting explicit functional goals rather than mer...
arXiv:2608. 07547v1 Announce Type: cross Abstract: Indoor scene layout generation is a challenging task in interior design.
arXiv:2606. 06390v1 Announce Type: cross Abstract: Indoor scene generation is crucial for robot simulation and modern interior design.
arXiv:2512. 16275v2 Announce Type: replace-cross Abstract: Automated floor plan generation lies at the intersection of combinatorial search, geometric constraint satisfaction, and functional design requirements -- a confluence that has historically resisted a unified computational treatment.
The paper investigates how conditioned floor plan generation models perform when applied to datasets from different regions, revealing significant performance drops due to domain shift. To address this, the authors create a large synthetic training set that enforces physical constraints while deliberately reducing architectural realism, and show that pre‑training on this data boosts zero‑shot cross‑domain performance and speeds up fine‑tuning in low‑data scenarios.
arXiv:2609.23386v1 Announce Type: new Abstract: Text-guided 3D building generation holds tremendous application potential, yet existing generative models typically output inseparable single meshes or...
arXiv:2609.07434v1 Announce Type: new Abstract: Natural-language Computer-Aided Design (CAD) code generation aims to turn design intent into executable and editable parametric programs. Large languag...
arXiv:2609.36064v1 Announce Type: cross Abstract: Foundation models are powerful generators, but many engineering domains require structured representations that general-purpose systems handle poorly...
Recent advances in large language models and programmatic CAD have significantly improved Text-to-CAD generation for individual parts. However, production-ready mechanical assembly generation remains largely unsolved.
arXiv:2607. 05123v1 Announce Type: new Abstract: Recent advances in large language models and programmatic CAD have significantly improved Text-to-CAD generation for individual parts.