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 https://react3d.github.io/
@article{arxiv.2510.11340,
title = {REACT3D: Recovering Articulations for Interactive Physical 3D Scenes},
author = {Zhao Huang and Boyang Sun and Alexandros Delitzas and Jiaqi Chen and Marc Pollefeys},
journal= {arXiv preprint arXiv:2510.11340},
year = {2026}
}
Comments
Accepted at IEEE Robotics and Automation Letters (RA-L)