Graph Canvas for Controllable 3D Scene Generation

Spatial intelligence is fundamental to AI systems that interact with the physical world, particularly in 3D scene generation and spatial comprehension. Current layout generation in 3D scene synthesis remains highly complex, often constrained by predefined datasets and limited dynamic adaptation to changing spatial relationships. In this paper, we propose GraphCanvas3D, a flexible, query-driven framework for controllable 3D scene generation. Unlike traditional methods that require retraining and predefined input masks for modifications, GraphCanvas3D provides a training-free solution supporting the generation of diverse scenes-both indoor and outdoor-through free manipulation of objects and scene elements. Our framework employs hierarchical, graph-driven scene descriptions, representing spatial elements as graph nodes and establishing coherent relationships among objects in 3D environments. The decoupled object representation enables flexible, on-the-fly scene adjustments and dynamic, customizable scene creation. Experimental results and user studies demonstrate that GraphCanvas3D improves usability, adaptability, and generalization across various 3D scene generation tasks, offering a powerful tool for scalable and diverse scene synthesis.