RenoLayout unifies home renovation and furniture placement with AI
RenoLayout: Unified Floorplan Renovation and Furniture Layout for Constraint-Aware Residential Design
Computation and Language
Summary
Designing functional home layouts that keep existing structures and place furniture well is tricky. The authors present RenoLayout, an AI method that takes an old home design and a set of design instructions to create a complete renovated floorplan with furniture. They built a detailed set of design points and a benchmark of real renovation cases to train and test their system. This helps combine two usually separate tasks—renovation and furniture arrangement—while respecting building constraints.
What this means in practice
- •For architectural design teams: Generate renovation plans that integrate structural changes with furniture layout under practical constraints from existing homes.
- •For interior designers: Create coordinated designs that simultaneously update floorplans and optimize furniture placement following specific design intentions.
Authors
Jingguo Liu, Yuanqing Liu, Chong Zhang, Yue Yang
Abstract
Residential design aims to transform existing homes into functional living spaces while satisfying diverse design preferences and physical constraints. However, existing floorplan generation methods typically assume empty building boundaries as input, whereas renovation requires reasoning over existing floorplans. Furthermore, floorplan renovation and furniture layout are commonly addressed as separate tasks, overlooking their inherent spatial dependency. Consequently, practical residential design must preserve immutable structural components while transforming editable spatial elements. It must also optimize furniture placement under design objectives and physical constraints. To address these challenges, we formulate residential design as a design-point-guided conditional generation task and propose RenoLayout, a framework that translates a pre-design residential state together with a design-point set into an executable Geometry-as-Code program. To enable controllable supervision, we construct a Design Map comprising 123 fine-grained design points covering structural renovation and furniture layout. We further introduce Difference-Grounded Design-Point Annotation to derive design-point sets automatically from before--after design pairs with VLM-based consistency verification. RenoLayout is trained using a three-stage CPT--SFT--RL pipeline, progressively improving spatial reasoning, instruction following, and constraint compliance. We also introduce the RenoLayout Benchmark, a set of 200 real-world before--after renovation cases with complete furniture layouts. To the best of our knowledge, it is the first benchmark for unified residential design. These cases include design-critical structural and building-service constraints.