Abstract
Prompt-driven scene synthesis allows users to generate complete 3D environments from textual descriptions. Current text-to-scene methods often struggle with complex geometries and object transformations, and tend to show weak adherence to complex instructions. We address these limitations by introducing Reason-3D, a text-to-scene model powered by large reasoning models (LRMs). Reason-3D integrates object retrieval using captions covering physical, functional, and contextual attributes. Reason-3D then places the selected objects based on implicit and explicit layout constraints, and refines their positions with collision-aware spatial reasoning. Evaluated on instructions ranging from simple to complex indoor configurations, Reason-3D significantly outperforms previous methods in human-rated visual fidelity, adherence to constraints, and asset retrieval quality. Beyond its contribution to the field of text-to-scene generation, our work showcases the advanced spatial reasoning abilities of modern LRMs. Additionally, we release the codebase to further the research in object retrieval and placement with LRMs.
Abstract (translated)
提示驱动的场景合成允许用户根据文本描述生成完整的3D环境。当前的文本到场景方法在处理复杂几何形状和对象变换时常常遇到困难，并且对复杂指令的遵循度较低。为了解决这些限制，我们引入了Reason-3D，这是一个由大型推理模型（LRMs）驱动的文本到场景模型。Reason-3D集成了基于描述物理属性、功能属性和上下文属性的对象检索功能。随后，它根据隐式和显式的布局约束放置选定对象，并通过碰撞感知的空间推理来优化它们的位置。 在从简单到复杂的室内配置指令上进行评估时，Reason-3D在人类评价的视觉逼真度、遵循约束的程度以及资产检索质量方面显著优于以前的方法。除了对文本到场景生成领域的贡献外，我们的工作还展示了现代LRMs先进的空间推理能力。此外，我们发布了代码库，以进一步推动LRMs在对象检索和放置方面的研究。
URL
https://arxiv.org/abs/2509.26091
