mlsanityData to 14 Oct 2026

DecoMind: A Generative AI System for Personalized Interior Design Layouts

arXiv 2508.16696 ↗First posted 22 Aug 2025cs.GR cs.AIcited by 0

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Abstract (arXiv)

This paper introduces a system for generating interior design layouts based on user inputs, such as room type, style, and furniture preferences. CLIP extracts relevant furniture from a dataset, and a layout that contains furniture and a prompt are fed to Stable Diffusion with ControlNet to generate a design that incorporates the selected furniture. The design is then evaluated by classifiers to ensure alignment with the user's inputs, offering an automated solution for realistic interior design.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 2 of its 6 references that are in the map. First posted on arXiv 22 Aug 2025.

Builds on

2 of 6 references.

References · 2

  1. 2023CLIP-Layout: Style-Consistent Indoor Scene Synthesis with Semantic Furniture Embeddingcited by 16
  2. 2019Furnishing Your Room by What You See: An End-to-End Furniture Set Retrieval Framework with Rich Annotated Benchmark Datasetcited by 4

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  1. 2026Directly from Alpha to Omega: Controllable End-to-End Vector Floor Plan Generation ↗
  2. 2024I-Design: Personalized LLM Interior Designer
  3. 2026StyleForge: Indoor Furniture Styling by Counterfactual Reasoning in a Hypergraph Field
  4. 2024Chat2Layout: Interactive 3D Furniture Layout with a Multimodal LLM
  5. 2023RoomDesigner: Encoding Anchor-latents for Style-consistent and Shape-compatible Indoor Scene Generation
  6. 2026Architect-Ant: Editable Automatic Furnishing of Architectural Floor Plans
  7. 2026Agentic Designer: Progressive Multi-Agent Collaboration for Structure-Aware Interior Layout Generation
  8. 2025OptiScene: LLM-driven Indoor Scene Layout Generation via Scaled Human-aligned Data Synthesis and Multi-Stage Preference Optimization