AccioScene: Compositional 3D Scene Generation via Graph Diffusion and Interaction-driven Critics
arXiv:2502.06819v2 Announce Type: replace Abstract: This paper presents a framework for generating 3D indoor scenes from text prompts. Existing methods often formulate scene synthesis as an object layout prediction problem conditioned on a single input modality, such as a text description, room shape, or scene graph. This design can lead to object collisions and limited functional plausibility, reducing its practical applicability. To address these limitations, we introduce a multi-stage pipeline that better reflects practical scene creation scenarios. Given a text prompt describing partial sc
The rapid advancement in AI, particularly in generative models and graph neural networks, enables more sophisticated approaches to 3D content creation, moving beyond simple object layout to functional and realistic scene generation.
Improved 3D scene generation from text prompts significantly lowers the barrier to creating virtual environments, impacting entertainment, design, simulation, and training industries.
Current 3D design workflows, often reliant on manual asset placement and detailed scripting, will be augmented or replaced by AI-driven, high-fidelity scene generation, making content creation faster and more accessible.
- · Game Development
- · Metaverse Platforms
- · Architectural Visualization
- · AI Content Creators
- · Manual 3D Asset Integrators
- · Low-fidelity 3D Content Providers
More realistic and diverse virtual worlds become easier and cheaper to create.
Demand for specialized 3D artists shifts from manual asset placement to curation and refinement of AI-generated scenes.
The proliferation of high-quality synthetic environments accelerates the development and adoption of AI agents by providing richer training and testing grounds.
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