CC-FMO: Camera-Conditioned Zero-Shot Single Image to 3D Scene Generation with Foundation Model Orchestration

• URL: http://arxiv.org/abs/2512.00493v1
• Date: Sat, 29 Nov 2025 14:01:13 GMT
• Title: CC-FMO: Camera-Conditioned Zero-Shot Single Image to 3D Scene Generation with Foundation Model Orchestration
• Authors: Boshi Tang, Henry Zheng, Rui Huang, Gao Huang,
• Abstract summary: High-quality 3D scene generation from a single image is crucial for AR/VR and embodied AI applications.<n>This paper introduces CC-FMO, a zero-shot, camera-conditioned pipeline for single-image to 3D scene generation.
• Score: 29.052223430061826
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: High-quality 3D scene generation from a single image is crucial for AR/VR and embodied AI applications. Early approaches struggle to generalize due to reliance on specialized models trained on curated small datasets. While recent advancements in large-scale 3D foundation models have significantly enhanced instance-level generation, coherent scene generation remains a challenge, where performance is limited by inaccurate per-object pose estimations and spatial inconsistency. To this end, this paper introduces CC-FMO, a zero-shot, camera-conditioned pipeline for single-image to 3D scene generation that jointly conforms to the object layout in input image and preserves instance fidelity. CC-FMO employs a hybrid instance generator that combines semantics-aware vector-set representation with detail-rich structured latent representation, yielding object geometries that are both semantically plausible and high-quality. Furthermore, CC-FMO enables the application of foundational pose estimation models in the scene generation task via a simple yet effective camera-conditioned scale-solving algorithm, to enforce scene-level coherence. Extensive experiments demonstrate that CC-FMO consistently generates high-fidelity camera-aligned compositional scenes, outperforming all state-of-the-art methods.

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