arXiv:2607.02407v1 Announce Type: new Abstract: Large Language Models (LLMs) have demonstrated remarkable capabilities in 3D indoor synthesis for Manhattan environments. However, existing methods often fail to capture plausible object layout patterns in non-Manhattan settings, primarily because they struggle to model non-orthogonal spatial relationships, leading to high geometric violations and low physical fidelity. To address this challenge, we propose SPG-Layout, a novel text-driven framework designed to generate physically plausible indoor scenes within complex non-Manhattan environments.
The increasing sophistication of Large Language Models (LLMs) and the demand for more realistic and complex 3D environments are driving advancements in text-driven scene synthesis.
This development expands the capabilities of AI in generating complex and non-standardized virtual environments, which is crucial for applications beyond simple 'Manhattan' grids.
AI-driven 3D scene generation can now better handle irregular architectural designs and non-orthogonal spatial relationships, reducing geometric violations and increasing physical fidelity.
- · Game development
- · Metaverse platforms
- · Architectural visualization
- · Robotics simulation
- · Manual 3D scene designers (for initial layouts)
- · Legacy 3D synthesis tools
- · Developers limited to orthogonal environments
More realistic and diverse virtual environments can be rapidly created from text descriptions.
This could accelerate the development of virtual reality training simulations and digital twins for complex real-world spaces.
The enhanced realism and adaptability of AI-generated environments may lead to a broader adoption of virtual platforms for design, planning, and operational tasks across various industries.
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Read at arXiv cs.AI