We present Artiverse, a diverse and physically grounded dataset of high-quality articulated 3D objects designed for realistic functional modeling and simulation. Artiverse contains 5.4K human-authored objects across a broad range of 88 categories, aggregated from multiple 3D static repositories. Objects are annotated with functional parts, interior structures, realistic kinematic relationships and articulated joints including multi-DoF joints, and physical attributes such as metric scale, material, and mass. We develop a semi-automated annotation pipeline that combines few-shot segmentation, geometric reasoning, and multi-stage human verification to achieve high-quality and efficient annotation, reducing manual annotation time by over 30%. We demonstrate the value of Artiverse on tasks of part mobility analysis, articulated object generation, and physics-based interaction. Artiverse provides a data resource to advance functional understanding for articulated objects.
@article{arxiv.2605.24403,
title = {Artiverse: A Diverse and Physically Grounded Dataset for Articulated Objects},
author = {Denys Iliash and Jiayi Liu and Egor Fokin and Qirui Wu and Ali Mahdavi-Amiri and Manolis Savva and Angel X. Chang},
journal= {arXiv preprint arXiv:2605.24403},
year = {2026}
}