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.
@article{arxiv.2603.19598,
title = {FlowScene: Style-Consistent Indoor Scene Generation with Multimodal Graph Rectified Flow},
author = {Zhifei Yang and Guangyao Zhai and Keyang Lu and YuYang Yin and Chao Zhang and Zhen Xiao and Jieyi Long and Nassir Navab and Yikai Wang},
journal= {arXiv preprint arXiv:2603.19598},
year = {2026}
}