---
license: other
license_name: mixed-per-object-see-notice
license_link: https://huggingface.co/datasets/Linzhan/Objaverse-XL-Rigged-Animated/blob/main/NOTICE.md
pretty_name: Objaverse-XL Rigged & Animated Subset
language:
- en
size_categories:
- 1K<n<10K
task_categories:
- text-to-3d
tags:
- 3d
- animation
- rigging
- skeletal-animation
- motion
- objaverse
- glb
- gltf
configs:
- config_name: assets
  default: true
  data_files: metadata.csv
- config_name: animations
  data_files: animations.csv
---

# Objaverse-XL Rigged & Animated Subset

Every asset here carries **both a skeleton and at least one animation clip**, selected from
[Objaverse / Objaverse-XL](https://objaverse.allenai.org/). Rigs range from 3 to 344 joints and
span characters as well as articulated rigid objects.

Objaverse-XL indexes over 10 million objects, but only a small fraction carry a usable rig **and**
motion on it. This subset isolates that fraction: every file was checked to contain at least one
skin with joints and at least one animation clip that actually drives them.

Assets are provided **exactly as downloaded** — no filtering, retargeting, canonicalization or
joint renaming has been applied — so you can run your own preprocessing on top. The accompanying
tables expose the structural properties needed to plan that preprocessing, such as which assets
have a disconnected skeleton or which clips are near-static.

| | |
|---|---|
| assets | 7,373 (23 GB) |
| animation clips | 16,190 (~21.0 h) |
| assets with both rig and animation | 7,373 (100%) |
| single kinematic tree | 7,128 (96.7%) — 245 need pruning |
| joints per asset | min 3 · median 44 · p95 78 · max 344 |
| clip duration | median 2.03 s · p95 17.1 s · max 745 s |
| near-static clips (< 0.1 s) | 803 |
| vertices per asset | median 6,628 · p95 88,124 · max 1.52 M |

## Contents

```
glb/               7,373 .glb   assets, original filenames preserved
metadata.csv       one row per asset
animations.csv     one row per clip
excluded.csv       18 assets known to defeat processing
scripts/           the glTF probe that derives both tables
```

`excluded.csv` records the assets a processing pipeline should skip: 3 that no importer reads
cleanly (corrupt rigs, singular transforms, NaN axis limits) and 15 whose animations are
entirely static or too short to use, with the reason per row.

Renders live in a separate repository,
[**Objaverse-XL-Rigged-Animated-Renders**](https://huggingface.co/datasets/Linzhan/Objaverse-XL-Rigged-Animated-Renders): a four-view MP4 of every animation clip
(10,355 folders) and a 2×2 rest-pose grid per asset (`tpose/`, 7,373 PNGs, previously hosted
here). Stems match, so both join back to the tables below without any name mangling.

Filenames are unchanged, so every asset maps back to its Objaverse entry:

| pattern | count | meaning |
|---|---|---|
| `<24-hex>_{fbx,glb,gltf}.glb` | 5,305 | Objaverse-XL (GitHub); suffix is the original format |
| `<32-hex>.glb` | 2,068 | Objaverse 1.0 / Sketchfab UID |

## metadata.csv

One row per asset, joined to `animations.csv` on `file`.

| column | meaning |
|---|---|
| `file` | path, e.g. `glb/00064e6f….glb` |
| `object_id`, `id_family` | Objaverse id and which family it came from |
| `source_format` | original format before GLB conversion; empty for Sketchfab |
| `num_vertices`, `num_meshes`, `num_nodes` | geometry size |
| `num_joints` | rig size |
| `num_skeleton_roots`, `single_tree` | number of kinematic trees; `single_tree` is `false` when the asset needs single-tree pruning |
| `num_animations`, `total_duration_sec`, `max_keyframes` | animation budget |
| `animated_joints` | joints driven by **any** clip — the union across all of them, so it can exceed the per-clip figure in `animations.csv` |
| `generator` | exporter string (`Sketchfab-*`, `Khronos glTF Blender I/O *`, …) |

## animations.csv

One row per animation, since motion datasets are counted in sequences rather than assets:
`file`, `object_id`, `clip_index`, `clip_name`, `duration_sec`, `keyframes`, `num_channels`,
`animated_nodes`, `animated_joints`, `drives_skeleton`.

`drives_skeleton` is the useful filter: **5,615 of 16,190 clips animate only non-joint nodes**
(object-level transforms rather than a character rig). Every asset has at least one clip that
does drive its skeleton.

## Usage

```python
from datasets import load_dataset

assets = load_dataset("Linzhan/Objaverse-XL-Rigged-Animated", "assets", split="train")
clips  = load_dataset("Linzhan/Objaverse-XL-Rigged-Animated", "animations", split="train")

# assets needing no single-tree pruning, with a humanoid-scale rig
clean = assets.filter(lambda r: r["single_tree"] == "true" and 20 <= r["num_joints"] <= 100)

# clips that drive a skeleton and are not near-static
usable = clips.filter(lambda r: r["drives_skeleton"] == "true" and r["duration_sec"] >= 0.5)
```

Both tables are derived from each file's glTF JSON chunk by `scripts/build_dataset.py`, so they
can be regenerated or extended without re-downloading anything.

## Processing it into motion data

This repository is the **raw** layer. The pipeline that turns it into canonicalized, text-paired motion clips is open at [`UniMate/data_process`](https://github.com/Friedrich-M/UniMate/tree/main/data_process): it exports each clip to NPZ, renders previews, captions them with a vision-language model, cleans the joint labels and derives the training features. The processed release built from it is [UniML3D](https://huggingface.co/datasets/Linzhan/UniML3D).

```bash
bash data_process/scripts/run_download.sh objaverse
bash data_process/scripts/run_export.sh objaverse
```

## Licensing and attribution

The assets in `glb/` were created by third parties and **retain their individual upstream
licences**, which are heterogeneous: various Creative Commons terms for Sketchfab objects, and
whatever applies to the GitHub-sourced ones. No blanket licence covers the collection and none is
asserted here — publishing them is not a licence grant. Resolve the licence for a given
`object_id` through the Objaverse-XL annotations before using or redistributing an asset.

The derived material — `metadata.csv`, `animations.csv`, `scripts/` and this card — is offered
under **ODC-BY 1.0**, matching the upstream Objaverse metadata. The renders in the companion
repository are **not** covered by that grant: they depict the assets themselves, so they inherit
each asset's upstream licence exactly as the GLB does.

Rights holders who want an asset removed can open an issue on this repository; see
[`NOTICE.md`](NOTICE.md) for the full statement and the takedown process.

## Citation

This dataset is part of **UniML3D**, the training corpus introduced in
[UniMate: One Unified Model to Animate Diverse Skeletons](https://arxiv.org/abs/2609.05415)
(SIGGRAPH Asia 2026; [paper page](https://huggingface.co/papers/2609.05415)). If you use it, please cite the paper:

```bibtex
@article{mou2026unimate,
  title   = {UniMate: One Unified Model to Animate Diverse Skeletons},
  author  = {Mou, Linzhan and Lei, Jiahui and Dou, Zhiyang and Cai, Chenyue and Song, Chaoyue and Finkelstein, Adam and Rusinkiewicz, Szymon},
  journal = {arXiv preprint arXiv:2609.05415},
  year    = {2026}
}
```

Please also cite this dataset repository and Objaverse-XL as the source of the assets:

```bibtex
@misc{objaverse_xl_rigged_animated,
  title  = {Objaverse-XL Rigged and Animated Subset},
  author = {Mou, Linzhan},
  year   = {2026},
  url    = {https://huggingface.co/datasets/Linzhan/Objaverse-XL-Rigged-Animated},
  note   = {Objaverse-XL assets carrying both a skeleton and animation, with derived metadata}
}

@inproceedings{deitke2023objaversexl,
  title     = {Objaverse-XL: A Universe of 10M+ 3D Objects},
  author    = {Deitke, Matt and Liu, Ruoshi and Wallingford, Matthew and others},
  booktitle = {Advances in Neural Information Processing Systems},
  pages     = {35799--35813},
  year      = {2023}
}
```