Datasets:

haoz19
/

RigMo-data

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.

📄 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.

# 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):

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 (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

@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}
}