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SceneConductor Generates 3D Scenes from Single Images Using Multi-Agent Orchestration

Researchers propose SceneConductor, a multi-agent orchestration framework that decomposes single-image 3D scene generation into three structured stages: initialization, environment construction, and refinement. It also introduces a geometry-aware layout predictor to reduce reliance on scene-level annotations. Experiments show it consistently outperforms prior approaches in geometric accuracy, spatial consistency, and perceptual realism.

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iGEN Editorial
June 16, 2026
SceneConductor Generates 3D Scenes from Single Images Using Multi-Agent Orchestration

Generating complete 3D scenes from a single image is a complex computer vision problem that requires inferring globally consistent geometry, object relationships, and environmental context from limited visual evidence. Existing methods often rely on holistic pipelines that demand extensive scene-level supervision, limiting their generalization to real-world environments. According to a research paper published on arXiv, a team of researchers has developed SceneConductor, a multi-agent orchestration framework that decomposes single-image 3D scene generation into three structured stages.

Multi-Agent Framework Architecture

SceneConductor operates in three stages:

  • Scene Initialization: Extracts image-derived object masks, builds object-level 3D representations, and predicts an initial spatial layout to form a coarse 3D scene.
  • Environment Construction: Leverages the initialization together with point-map geometry to build an environmental scaffold of supporting surfaces, room boundaries, materials, and illumination.
  • Multi-Agent Refinement: A planner agent identifies structural and visual inconsistencies, applies simple corrections directly, and dispatches specialist agents for complex localized revisions that are reintegrated into the global scene.

Geometry-Aware Layout Predictor

To provide reliable structural initialization while reducing reliance on scene-level annotations, the research introduces a geometry-aware layout predictor supervised by sparse geometric priors derived from point maps. Unlike fully supervised layout generators, this predictor can be trained from segmentation-level data and generalizes robustly to diverse real-world scenes.

Experimental Results

Extensive experiments on benchmark datasets show that SceneConductor consistently outperforms prior approaches in geometric accuracy, spatial consistency, and perceptual realism. The method addresses the challenge of inferring from inherently ambiguous visual evidence by decomposing the task into manageable subproblems handled by specialized agents.

The framework's modular design could potentially be adapted for enterprise applications requiring 3D scene understanding, such as logistics planning or warehouse layout optimization, though the paper focuses on general scene generation. The research demonstrates that breaking down a holistic task into structured, agent-based pipelines can improve generalization and reduce supervision requirements.

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