README.md

---
license: other
license_name: rigmo-research-only
license_link: https://rigmo-page.github.io/
pretty_name: RigMo Mesh-Motion Dataset
task_categories:
  - other
tags:
  - 3d
  - 4d
  - animation
  - rigging
  - mesh
  - motion
extra_gated_prompt: >-
  This dataset is a preprocessed derivative of DeformingThings4D, Objaverse-XL,
  and TrueBones, provided for non-commercial academic research only. By
  requesting access you agree to (1) use the data solely for non-commercial
  research, (2) comply with the original licenses and terms of DeformingThings4D,
  Objaverse-XL, and TrueBones, and (3) cite the RigMo paper and the original
  datasets in any resulting work. The authors make no warranty and accept no
  liability for use of this data.
extra_gated_fields:
  Full name: text
  Affiliation: text
  Purpose of use: text
  I will use this data for non-commercial research only: checkbox
  I agree to comply with the original dataset licenses (DeformingThings4D, Objaverse-XL, TrueBones): checkbox
  I agree to cite the RigMo paper and the source datasets: checkbox
---

# RigMo Mesh-Motion Dataset

Preprocessed mesh-sequence data used to train the **RigMo-VAE** from
[*RigMo: Unifying Rig and Motion Learning for Generative Animation*](https://arxiv.org/abs/2601.06378).

- 📄 Paper: https://arxiv.org/abs/2601.06378
- 🌐 Project page: https://rigmo-page.github.io/
- 💻 Code: https://github.com/haoz19/RigMo

**Scale:** ~18,985 sequences · ~534k `.npz` frames · ~46 GiB.

## Download

The data ships as **10 `.tar.zst` archives** (one per group) so the half-million
small frame files transfer efficiently. Download them, extract into a single
folder, and point the training config at that folder.

```bash
# 1. Download all archives (requires `pip install huggingface_hub` and access approval)
huggingface-cli download haoz19/RigMo-data \
  --repo-type dataset --local-dir rigmo_data_archives

# 2. Extract every archive into ./rigmo_data  (needs `zstd` + `tar`)
mkdir -p rigmo_data
for f in rigmo_data_archives/*.tar.zst; do
  tar -I zstd -xf "$f" -C rigmo_data
done

# 3. (optional) remove the archives to reclaim space
# rm -rf rigmo_data_archives
```

After extraction you get the layout the training code expects:

```
rigmo_data/
├── deformingthings4d/      # sequences derived from DeformingThings4D
├── objxl_rendered_0_2500/  # Objaverse-XL render shards (8 dirs)
├── objxl_rendered_2500_5000/
├── ...
└── val/                    # held-out validation split (100 sequences)
```

Then train (see the [code repo](https://github.com/haoz19/RigMo)):

```bash
python train.py --config configs/rigmo_vae_temporal_single_node.yaml --train \
  data.root_dir=/abs/path/to/rigmo_data
```

| Archive | Description |
|---------|-------------|
| `deformingthings4d.tar.zst` | Sequences derived from DeformingThings4D |
| `objxl_rendered_*.tar.zst`  | Sequences derived from Objaverse-XL renders (8 shards) |
| `val.tar.zst`               | Held-out validation split (100 sequences) |

## Format

Each sequence is a directory of per-frame `.npz` files:

```
<sequence_name>/
├── frame_0000.npz     # vertices [N, 3] float32 · neighbor_idx [N, k] int64
├── frame_0001.npz
└── ...
```

| Key | Shape | Description |
|-----|-------|-------------|
| `vertices` | `[N, 3]` `float32` | per-frame vertex positions (here `N = 5000`) |
| `neighbor_idx` | `[N, k]` `int64` | per-vertex mesh neighbors (mesh topology) |

Sequences are normalized at load time so the first frame's bounding box maps to a
unit cube centered at the origin. See the
[training code](https://github.com/haoz19/RigMo) (`FullMeshMotionNPZ-datamodule`)
for exact loading details. The training data module recursively discovers sequence
directories and reserves `val/` (and `test/`, if present) as held-out splits.

## Licensing & attribution

This is a **derivative** dataset for **non-commercial academic research only**.
It is built from:

- **DeformingThings4D** — academic / non-commercial; subject to its original terms.
- **Objaverse-XL** — ODC-BY; individual assets retain their own licenses.
- **TrueBones** — subject to TrueBones' own terms.

You must comply with all original dataset licenses. Access is gated; requesting
access constitutes agreement to the terms above.

## Citation

```bibtex
@article{zhang2026rigmo,
  title   = {RigMo: Unifying Rig and Motion Learning for Generative Animation},
  author  = {Zhang, Hao and Luo, Jiahao and Wan, Bohui and Zhao, Yizhou and Li, Zongrui
             and Vasilkovsky, Michael and Wang, Chaoyang and Wang, Jian and Ahuja, Narendra
             and Zhou, Bing},
  journal = {arXiv preprint arXiv:2601.06378},
  year    = {2026}
}
```