Artiverse: A Diverse and Physically Grounded Dataset for Articulated Objects
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866917527047634944 |
|---|---|
| author | Iliash, Denys Liu, Jiayi Fokin, Egor Wu, Qirui Mahdavi-Amiri, Ali Savva, Manolis Chang, Angel X. |
| author_facet | Iliash, Denys Liu, Jiayi Fokin, Egor Wu, Qirui Mahdavi-Amiri, Ali Savva, Manolis Chang, Angel X. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_24403 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Artiverse: A Diverse and Physically Grounded Dataset for Articulated Objects Iliash, Denys Liu, Jiayi Fokin, Egor Wu, Qirui Mahdavi-Amiri, Ali Savva, Manolis Chang, Angel X. Computer Vision and Pattern Recognition 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. |
| title | Artiverse: A Diverse and Physically Grounded Dataset for Articulated Objects |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2605.24403 |