Articulation-Gen: 3D Part Segmentation and Articulated Object Generation

Abstract

Recent advances in 3D content generation, particularly 3D Gaussian Splatting (3DGS) and diffusion models, have significantly improved the synthesis of static shapes and textures. However, the modeling of dynamic articulations remains a significant challenge. Existing datasets lack physics-aware joint annotations, segmentation methods overlook kinematic constraints, and procedural generation techniques often prioritize space coverage over physical plausibility and visual realism. Motivated by these challenges, we propose Articulation-Gen, a scalable and robust framework for generating physically compliant, multi-joint 3D objects. Our approach comprises three components: (1) a 3D semantic segmentation module that integrates 2D visual models (SAM2 and DINO) to achieve 91.4% part segmentation accuracy by resolving occlusions via multi-view fusion with semantic consistency; (2) a physics-guided joint optimizer that combines spatial sampling with heuristic search to reach 93.7% axis alignment accuracy, representing a 20.6% improvement; and (3) an LLM-augmented URDF synthesis mechanism that automatically produces physically plausible kinematic descriptions with language annotations, thereby improving generation accuracy by 87.5%. Leveraging existing 3D asset datasets and generation techniques, we further construct a large-scale articulation asset dataset comprising 10.6K articulated objects with 45.2K validated joints. This dataset enables faster articulated asset generation while ensuring URDF compliance. By proposing our pipeline and dataset, this work provides foundational tools for physics-based computer graphics and embodied AI, advancing the frontiers of 3D content creation and robotic simulation.