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Add T-pose render grids and excluded.csv; document them in the card
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---
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
tpose/ 7,373 .png rest-pose render grid per asset, same stems
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.
`tpose/<stem>.png` is a 1024×1024 2×2 grid of the asset's rest pose — front and back on the
top row, left and right below — rendered (EEVEE) from its GLB, one per asset in `glb/`.
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.
## 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.
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
If you use this subset, please cite 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}
}
```