Abstract
Interactive 3D scenes are increasingly vital for embodied intelligence, yet existing datasets remain limited due to the labor-intensive process of annotating part segmentation, kinematic types, and motion trajectories. We present REACT3D, a scalable zero-shot framework that converts static 3D scenes into simulation-ready interactive replicas with consistent geometry, enabling direct use in diverse downstream tasks. Our contributions include: (i) openable-object detection and segmentation to extract candidate movable parts from static scenes, (ii) articulation estimation that infers joint types and motion parameters, (iii) hidden-geometry completion followed by interactive object assembly, and (iv) interactive scene integration in widely supported formats to ensure compatibility with standard simulation platforms. We achieve state-of-the-art performance on detection/segmentation and articulation metrics across diverse indoor scenes, demonstrating the effectiveness of our framework and providing a practical foundation for scalable interactive scene generation, thereby lowering the barrier to large-scale research on articulated scene understanding. Our project page is \textit{\hypersetup{urlcolor=black}\href{this https URL}{this http URL}}.
Abstract (translated)
交互式3D场景对于具身智能（embodied intelligence）的重要性日益增加，然而由于对部件分割、运动类型和轨迹标注的劳动密集型过程，现有的数据集仍然受限。我们提出了REACT3D，这是一个可扩展的零样本框架，能够将静态3D场景转换为具有连贯几何结构的交互式副本，可以直接用于各种下游任务中。我们的贡献包括： (i) 可开启物体检测和分割：从静态场景中提取候选移动部件； (ii) 轴组估计：推断关节类型和运动参数； (iii) 隐藏几何补全并进行交互对象组装； (iv) 在广泛支持的格式下实现交互式场景集成，以确保与标准仿真平台兼容。 我们在各种室内场景上的检测/分割和轴组度量上取得了最先进的性能，展示了我们框架的有效性，并为大规模交互式场景生成提供了一个实用的基础，从而降低了研究复杂连贯场景理解的大规模障碍。我们的项目页面位于 [此链接](this https URL)。
URL
https://arxiv.org/abs/2510.11340
