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Agentic Designer: Progressive Multi-Agent Collaboration for Structure-Aware Interior Layout Generation
Summary
Agentic Designer, a new framework, generates realistic interior furniture layouts by iteratively verifying designs against architectural constraints. This approach prevents collisions and ensures functional arrangements, outperforming existing methods.
Area of Science:
- Computer Science
- Artificial Intelligence
- Computational Design
Background:
- Automated interior design faces challenges in adhering to architectural constraints, leading to collisions and infeasible layouts.
- Current one-shot generation methods using diffusion models or Large Language Models (LLMs) lack intermediate constraint verification.
Purpose of the Study:
- To propose Agentic Designer, a novel multi-agent framework for structure-aware interior layout generation.
- To address limitations in existing methods by introducing iterative constraint verification and correction.
Main Methods:
- Agentic Designer employs a progressive, multi-agent system with Generator, Evaluator, and Refiner agents.
- A Progressive Consensus Mechanism ensures stepwise geometric validation and adjustment before committing placements.
- The InStruct benchmark, featuring 18,000+ samples and structure-centric metrics, was developed for evaluation.
Main Results:
- Agentic Designer significantly improves adherence to structural constraints compared to state-of-the-art methods.
- The framework demonstrates enhanced functional design coherence in generated interior layouts.
- Quantitative, qualitative, and user study evaluations confirm the method's superiority.
Conclusions:
- Agentic Designer offers a robust solution for generating realistic and architecturally compliant interior furniture layouts.
- The iterative, constraint-verified approach effectively prevents errors and improves overall layout quality.
- This framework advances automated spatial design by integrating progressive consensus and specialized agents.
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