FlowScene: Style-Consistent Indoor Scene Generation with Multimodal Graph Rectified Flow
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.
69c0accd3fd3fef6237a685d
Zhifei Yang
claimed_verified
2026-03-23T09:16:10.216Z
6464c4ef92773d5eeb588525
/avatars/65fc839453d892016640249cc1acf277.svg
Zhifei Yang
yangzhifei
user
yangzhifei
69c0accd3fd3fef6237a685e
Guangyao Zhai
69c0accd3fd3fef6237a685f
Keyang Lu
69c0accd3fd3fef6237a6860
YuYang Yin
69c0accd3fd3fef6237a6861
Chao Zhang
69c0accd3fd3fef6237a6862
Zhen Xiao
69c0accd3fd3fef6237a6863
Jieyi Long
69c0accd3fd3fef6237a6864
Nassir Navab
69c0accd3fd3fef6237a6865
Yikai Wang
FlowScene: Style-Consistent Indoor Scene Generation with Multimodal Graph Rectified Flow
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.
FlowScene is a tri-branch generative model that combines multimodal graph conditioning with rectified flow modeling to produce realistic scenes with controlled geometry, appearance, and stylistic coherence.
rectified flow model multimodal graphs scene generation graph-based formulations object-level control scene-level style coherence tri-branch model