FlowScene: Style-Consistent Indoor Scene Generation with Multimodal Graph Rectified Flow

Zhifei Yang; Guangyao Zhai; Keyang Lu; YuYang Yin; Chao Zhang; Zhen Xiao; Jieyi Long; Nassir Navab; Yikai Wang

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arXiv · 2603.19598

FlowScene: Style-Consistent Indoor Scene Generation with Multimodal Graph Rectified Flow

Zhifei Yang·Guangyao Zhai·Keyang Lu·YuYang Yin·Chao Zhang·Zhen Xiao·Jieyi Long·Nassir Navab·Yikai Wang

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Abstract

Scene generation has extensive industrial applications, demanding both high realism and precise control over geometry and appearance. Language-driven retrieval methods compose plausible scenes from a large object database, but overlook object-level control and often fail to enforce scene-level style coherence. Graph-based formulations offer higher controllability over objects and inform holistic consistency by explicitly modeling relations, yet existing methods struggle to produce high-fidelity textured results, thereby limiting their practical utility. We present FlowScene, a tri-branch scene generative model conditioned on multimodal graphs that collaboratively generates scene layouts, object shapes, and object textures. At its core lies a tight-coupled rectified flow model that exchanges object information during generation, enabling collaborative reasoning across the graph. This enables fine-grained control of objects' shapes, textures, and relations while enforcing scene-level style coherence across structure and appearance. Extensive experiments show that FlowScene outperforms both language-conditioned and graph-conditioned baselines in terms of generation realism, style consistency, and alignment with human preferences.

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Zhifei Yang, Guangyao Zhai, Keyang Lu, YuYang Yin, Chao Zhang, Zhen Xiao, Jieyi Long, Nassir Navab, Yikai Wang. 2026-03-20. FlowScene: Style-Consistent Indoor Scene Generation with Multimodal Graph Rectified Flow. https://arxiv.org/abs/2603.19598

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