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.
Abstract (translated)
场景生成具有广泛的工业应用，既要求高真实感，又需对几何与外观进行精确控制。基于语言检索的方法虽能从大型物体数据库中组合出合理场景，却忽略了物体级控制，且常难以保证场景级风格一致性。基于图的方法通过显式建模关系，在物体控制与整体一致性方面更具优势，但现有方法难以生成高保真纹理结果，限制了其实用价值。本文提出FlowScene，一个基于多模态图条件的三分支场景生成模型，可协同生成场景布局、物体形状与物体纹理。其核心是一个紧密耦合的整流流模型，在生成过程中交换物体信息，实现跨图的协同推理。该方法能在精细控制物体形状、纹理及关系的同时，确保结构与外观层面的场景级风格一致性。大量实验表明，FlowScene在生成真实感、风格一致性及符合人类偏好方面，均优于语言条件与图条件基线方法。
URL
https://arxiv.org/abs/2603.19598
