Graph Canvas for Controllable 3D Scene Generation

TL;DR

GraphCanvas3D is a flexible framework for controllable 3D scene generation that uses hierarchical graph descriptions and in-context learning to enable dynamic, adaptable, and temporally-aware scene creation without retraining.

Contribution

We introduce GraphCanvas3D, a novel framework that allows real-time, adaptable 3D scene generation using graph-based descriptions and in-context learning, surpassing traditional static methods.

Findings

Enhances usability and flexibility in 3D scene generation.

Supports 4D scene creation with temporal dynamics.

Enables seamless object manipulation without retraining.

Abstract

Spatial intelligence is foundational to AI systems that interact with the physical world, particularly in 3D scene generation and spatial comprehension. Current methodologies for 3D scene generation often rely heavily on predefined datasets, and struggle to adapt dynamically to changing spatial relationships. In this paper, we introduce GraphCanvas3D, a programmable, extensible, and adaptable framework for controllable 3D scene generation. Leveraging in-context learning, GraphCanvas3D enables dynamic adaptability without the need for retraining, supporting flexible and customizable scene creation. Our framework employs hierarchical, graph-driven scene descriptions, representing spatial elements as graph nodes and establishing coherent relationships among objects in 3D environments. Unlike conventional approaches, which are constrained in adaptability and often require predefined input masks or retraining for modifications, GraphCanvas3D allows for seamless object manipulation and scene adjustments on the fly. Additionally, GraphCanvas3D supports 4D scene generation, incorporating temporal dynamics to model changes over time. Experimental results and user studies demonstrate that GraphCanvas3D enhances usability, flexibility, and adaptability for scene generation. Our code and models are available on the project website: https://github.com/ILGLJ/Graph-Canvas.