SceneConductor: Multi-Agent Framework for 3D Scene Generation from Single Images
Researchers have developed SceneConductor, a multi-agent orchestration framework that generates complete 3D scenes from single images by decomposing the task into three structured stages: initialization, environment construction, and refinement. The method addresses limitations of existing approaches by reducing reliance on extensive scene-level supervision and improving generalization to complex real-world environments. This advance could improve applications requiring 3D scene understanding from limited visual input, such as robotics, virtual reality, and autonomous systems.
SceneConductor proposes a novel approach to single-image 3D scene generation by breaking down the complex task into manageable stages rather than using monolithic pipelines. The framework first extracts object masks and builds initial 3D representations with spatial layout prediction, then constructs environmental scaffolding including surfaces, boundaries, materials, and lighting, and finally uses a planner agent to identify inconsistencies and dispatch specialist agents for localized corrections. A key innovation is the geometry-aware layout predictor that can be trained from segmentation-level data rather than requiring full scene-level annotations, enabling better generalization. Experiments on benchmark datasets demonstrate improvements in geometric accuracy, spatial consistency, and perceptual realism compared to prior methods.
What's missing
The paper does not discuss computational costs, inference time, or practical deployment considerations. Additionally, while the method shows improvements on benchmark datasets, real-world applicability beyond tested scenarios and failure modes are not detailed in the abstract.
What different sources said
- arXiv cs.AICenter
SceneConductor: 3D Scene Generation from Single Image with Multi-Agent Orchestration
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