metadata

license: cc-by-4.0
tags:
  - automatic-rigging
size_categories:
  - 10K<n<100K

MagicArticulate: Make Your 3D Models Articulation-Ready

Chaoyue Song^1,2, Jianfeng Zhang^2*, Xiu Li², Fan Yang^1,2, Yiwen Chen¹, Zhongcong Xu²,
Jun Hao Liew², Xiaoyang Guo², Fayao Liu³, Jiashi Feng², Guosheng Lin^1*
*Corresponding authors
¹Nanyang Technological University ²Bytedance Seed ³A*STAR

CVPR 2025

Project | Paper | Code | Video

Update

2025.3.22: Update the preprocessed data to include vertex normals. The data size will increase by 10GB after adding the normals. To save data load time during training, we suggest removing some unused information.
2025.3.20: Release Articulation-XL2.0 preprocessed data (a NPZ file includes vertices, faces, joints, bones, skinning weights, uuid, etc.).
2025.2.16: Release metadata for Articulation-XL2.0!

Overview

This repository introduces Articulation-XL2.0, a large-scale dataset featuring over 48K 3D models with high-quality articulation annotations, filtered from Objaverse-XL. Compared to version 1.0, Articulation-XL2.0 includes 3D models with multiple components. For further details, please refer to the statistics below.

Note: The data with rigging has been deduplicated (over 150K). The quality of most data has been manually verified.

Metadata

We provide the following information in the metadata of Articulation-XL2.0.

uuid,source,vertex_count,face_count,joint_count,bone_count,category_label,fileType,fileIdentifier

Preprocessed data

We provide the preprocessed data that saved in NPZ files, which contain the following information:

'vertices', 'faces', 'normals', 'joints', 'bones', 'root_index', 'uuid', 'pc_w_norm', 'joint_names', 'skinning_weights_value', 'skinning_weights_row', 'skinning_weights_col', 'skinning_weights_shape'

Citation

@article{song2025magicarticulate,
      title={MagicArticulate: Make Your 3D Models Articulation-Ready}, 
      author={Chaoyue Song and Jianfeng Zhang and Xiu Li and Fan Yang and Yiwen Chen and Zhongcong Xu and Jun Hao Liew and Xiaoyang Guo and Fayao Liu and Jiashi Feng and Guosheng Lin},
      journal={arXiv preprint arXiv:2502.12135},
      year={2025},
}