Datasets:

RoMoDataset
/

RoMo-Hands

---
license: cc-by-nc-4.0
task_categories:
  - text-to-3d
language:
  - en
tags:
  - hand-motion
  - motion-generation
  - text-to-motion
  - mano
  - bimanual
  - litdata
  - 3d-motion
pretty_name: RoMo Hands
size_categories:
  - 100K<n<1M
---
# RoMo Hands

**RoMo Hands: A Large Scale Richly Organized Text to Hand Motion Dataset** (NeurIPS 2026)

[Project page](https://romodataset.github.io/romo-hands/) ·
[Paper](https://romodataset.github.io/romo-hands/static/paper/RoMo_Hands.pdf) ·
[Motion Toolbox](https://github.com/RoMoDataset/motion-toolbox) ·
[RoMo (full body)](https://huggingface.co/datasets/RoMoDataset/RoMo-SMPL)

RoMo Hands is an in-the-wild, text-paired dataset of bimanual hand motion: 541,929
sequences, 884 hours, and 95.5M frames at 30 fps. Every sequence is paired with five
captions of increasing detail, from a short action tag to a paragraph describing finger
articulation, temporal dynamics, and spatial relations.

## Summary

| Property | Value |
|---|---|
| Sequences | 541,929 |
| Duration | 884 h (95.5M frames at 30 fps; mean clip 5.9 s) |
| Splits (train / val / test) | 464,278 / 50,985 / 26,666 (85% / 10% / 5%, segment level) |
| Motion | 393-D bimanual representation per frame (layout below) |
| Captions | 5 per sequence: tag, short, medium, detailed, paragraph |
| License | CC BY-NC 4.0, non-commercial research use only |

Clips range from 30 to 600 frames. The paper's benchmark scores only the 17,348 test clips of
40–196 frames (listed in `splits/benchmark_test.txt`); evaluating on the full test split will not
reproduce the paper's numbers. During training, clips longer than 196 frames are randomly cropped
to 196 frames and clips shorter than 40 frames are zero-padded to 40.

## Files

The data is stored in [LitData](https://github.com/Lightning-AI/litdata) streaming format:

```
train/train/   index.json + chunk-*.bin
val/val/       index.json + chunk-*.bin
test/test/     index.json + chunk-*.bin
Mean.npy       per-dimension mean of the 393-D representation
Std.npy        per-dimension standard deviation (constant dimensions set to 1)
```

Each sample is a dict:

```python
{
    "motion": np.ndarray,       # (T, 393) float32, unnormalized
    "texts": list[str],         # [tag, short, medium, detailed, paragraph]
    "sample_path": str,         # source segment identifier
}
```

## Video-disjoint splits

The released split is assigned per segment, so some source videos appear in more than one
split (15,152 videos are shared by train and test). For leakage-free use, `splits/` provides:

- `video_disjoint_test.txt`: the 7,679 released test segments (7,596 videos) whose source
  video appears in neither train nor val. Use it to evaluate models trained on the released
  train split.
- `video_level/{train,val,test}.txt`: a strict per-source-video split (461,203 / 53,935 /
  26,791 segments) for training new models; each video is assigned by
  `sha256(video_id) mod 100` (<85 train, <95 val, else test).
- `benchmark_test.txt`: the 17,348 test segments of 40–196 frames used by the paper's benchmark.
- `split_stats.json`: segment and video counts, and the video-id rule.

Segment ids match each sample's `sample_path`; the source video id is the prefix before
`_segment_`.

## Loading

```python
from huggingface_hub import snapshot_download
from litdata import StreamingDataset
root = snapshot_download("RoMoDataset/RoMo-Hands", repo_type="dataset", allow_patterns="test/*")
dataset = StreamingDataset(f"{root}/test/test")   # 26,666 clips; use train/train or val/val likewise
sample = dataset[0]
motion = sample["motion"]        # (T, 393)
tag, short, medium, detailed, paragraph = sample["texts"]
```

## Motion representation

Each frame is a 393-D vector over the 32 MANO joints of both hands (joints 0–15: left hand,
16–31: right hand; wrists at 0 and 16). The root is the midpoint of the two wrists, and the
heading is a yaw-only rotation that aligns the inter-wrist segment with the world x-axis.

| Dims | Channel | Width |
|---|---|---|
| 0–1 | Root XZ velocity, heading-aligned | 2 |
| 2 | Root height (absolute world y) | 1 |
| 3–8 | Heading rotation, 6D | 6 |
| 9–104 | Joint positions, root-relative and heading-aligned (32 × 3) | 96 |
| 105–200 | Joint velocities, finite differences (32 × 3) | 96 |
| 201–392 | Joint rotations, 6D (32 × 6); wrists in the heading frame, fingers parent-relative | 192 |

See Section 3.3 and Appendix B of the paper for the full definition and the type-aware
normalization used for training.

## Captions

| Level | Mean words |
|---|---|
| L0 tag | 3.5 |
| L1 short | 13.6 |
| L2 medium | 49.4 |
| L3 detailed | 92.5 |
| L4 paragraph | 149.3 |

Captions were generated with Qwen3.5-27B from the source video. A 250-clip human study
(12 raters, 370 ratings) rated overall action match at 4.16 / 5 on average.

## How it was built

RoMo Hands extends the [RoMo](https://huggingface.co/datasets/RoMoDataset/RoMo-SMPL)
collection pipeline. Hands are recovered per frame with WiLoR and anchored to RoMo's
temporally consistent body wrists, short gaps are interpolated, and clips are filtered
by per-hand detection ratio and a dexterity score before captioning.

## License

CC BY-NC 4.0: free for non-commercial research use with attribution; commercial use is
not permitted. Reconstructing hand meshes requires the MANO model, which is distributed
separately under its own license at <https://mano.is.tue.mpg.de/>.

## Citation

```bibtex
@inproceedings{BenShabat2026RoMoHands,
  author    = {Ben-Shabat, Yizhak and Zhang, Jiahao and Liu, Joseph and Moon, Seonghyeon and Lee, Young-Yoon and Jiang, Haomiao and Jacob, Oren and Kapadia, Mubbasir},
  title     = {{RoMo Hands}: A Large Scale Richly Organized Text to Hand Motion Dataset},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year      = {2026},
}
```

	---
	license: cc-by-nc-4.0
	task_categories:
	- text-to-3d
	language:
	- en
	tags:
	- hand-motion
	- motion-generation
	- text-to-motion
	- mano
	- bimanual
	- litdata
	- 3d-motion
	pretty_name: RoMo Hands
	size_categories:
	- 100K<n<1M
	---

	# RoMo Hands

	RoMo Hands: A Large Scale Richly Organized Text to Hand Motion Dataset (NeurIPS 2026)

	[Project page](https://romodataset.github.io/romo-hands/) ·
	[Paper](https://romodataset.github.io/romo-hands/static/paper/RoMo_Hands.pdf) ·
	[Motion Toolbox](https://github.com/RoMoDataset/motion-toolbox) ·
	[RoMo (full body)](https://huggingface.co/datasets/RoMoDataset/RoMo-SMPL)

	RoMo Hands is an in-the-wild, text-paired dataset of bimanual hand motion: 541,929
	sequences, 884 hours, and 95.5M frames at 30 fps. Every sequence is paired with five
	captions of increasing detail, from a short action tag to a paragraph describing finger
	articulation, temporal dynamics, and spatial relations.

	## Summary

	\| Property \| Value \|
	\|---\|---\|
	\| Sequences \| 541,929 \|
	\| Duration \| 884 h (95.5M frames at 30 fps; mean clip 5.9 s) \|
	\| Splits (train / val / test) \| 464,278 / 50,985 / 26,666 (85% / 10% / 5%, segment level) \|
	\| Motion \| 393-D bimanual representation per frame (layout below) \|
	\| Captions \| 5 per sequence: tag, short, medium, detailed, paragraph \|
	\| License \| CC BY-NC 4.0, non-commercial research use only \|

	Clips range from 30 to 600 frames. The paper's benchmark scores only the 17,348 test clips of
	40–196 frames (listed in `splits/benchmark_test.txt`); evaluating on the full test split will not
	reproduce the paper's numbers. During training, clips longer than 196 frames are randomly cropped
	to 196 frames and clips shorter than 40 frames are zero-padded to 40.

	## Files

	The data is stored in [LitData](https://github.com/Lightning-AI/litdata) streaming format:

	```
	train/train/ index.json + chunk-*.bin
	val/val/ index.json + chunk-*.bin
	test/test/ index.json + chunk-*.bin
	Mean.npy per-dimension mean of the 393-D representation
	Std.npy per-dimension standard deviation (constant dimensions set to 1)
	```

	Each sample is a dict:

	```python
	{
	"motion": np.ndarray, # (T, 393) float32, unnormalized
	"texts": list[str], # [tag, short, medium, detailed, paragraph]
	"sample_path": str, # source segment identifier
	}
	```

	## Video-disjoint splits

	The released split is assigned per segment, so some source videos appear in more than one
	split (15,152 videos are shared by train and test). For leakage-free use, `splits/` provides:

	- `video_disjoint_test.txt`: the 7,679 released test segments (7,596 videos) whose source
	video appears in neither train nor val. Use it to evaluate models trained on the released
	train split.
	- `video_level/{train,val,test}.txt`: a strict per-source-video split (461,203 / 53,935 /
	26,791 segments) for training new models; each video is assigned by
	`sha256(video_id) mod 100` (<85 train, <95 val, else test).
	- `benchmark_test.txt`: the 17,348 test segments of 40–196 frames used by the paper's benchmark.
	- `split_stats.json`: segment and video counts, and the video-id rule.

	Segment ids match each sample's `sample_path`; the source video id is the prefix before
	`_segment_`.

	## Loading

	```python
	from huggingface_hub import snapshot_download
	from litdata import StreamingDataset

	root = snapshot_download("RoMoDataset/RoMo-Hands", repo_type="dataset", allow_patterns="test/*")
	dataset = StreamingDataset(f"{root}/test/test") # 26,666 clips; use train/train or val/val likewise
	sample = dataset[0]
	motion = sample["motion"] # (T, 393)
	tag, short, medium, detailed, paragraph = sample["texts"]
	```

	## Motion representation

	Each frame is a 393-D vector over the 32 MANO joints of both hands (joints 0–15: left hand,
	16–31: right hand; wrists at 0 and 16). The root is the midpoint of the two wrists, and the
	heading is a yaw-only rotation that aligns the inter-wrist segment with the world x-axis.

	\| Dims \| Channel \| Width \|
	\|---\|---\|---\|
	\| 0–1 \| Root XZ velocity, heading-aligned \| 2 \|
	\| 2 \| Root height (absolute world y) \| 1 \|
	\| 3–8 \| Heading rotation, 6D \| 6 \|
	\| 9–104 \| Joint positions, root-relative and heading-aligned (32 × 3) \| 96 \|
	\| 105–200 \| Joint velocities, finite differences (32 × 3) \| 96 \|
	\| 201–392 \| Joint rotations, 6D (32 × 6); wrists in the heading frame, fingers parent-relative \| 192 \|

	See Section 3.3 and Appendix B of the paper for the full definition and the type-aware
	normalization used for training.

	## Captions

	\| Level \| Mean words \|
	\|---\|---\|
	\| L0 tag \| 3.5 \|
	\| L1 short \| 13.6 \|
	\| L2 medium \| 49.4 \|
	\| L3 detailed \| 92.5 \|
	\| L4 paragraph \| 149.3 \|

	Captions were generated with Qwen3.5-27B from the source video. A 250-clip human study
	(12 raters, 370 ratings) rated overall action match at 4.16 / 5 on average.

	## How it was built

	RoMo Hands extends the [RoMo](https://huggingface.co/datasets/RoMoDataset/RoMo-SMPL)
	collection pipeline. Hands are recovered per frame with WiLoR and anchored to RoMo's
	temporally consistent body wrists, short gaps are interpolated, and clips are filtered
	by per-hand detection ratio and a dexterity score before captioning.

	## License

	CC BY-NC 4.0: free for non-commercial research use with attribution; commercial use is
	not permitted. Reconstructing hand meshes requires the MANO model, which is distributed
	separately under its own license at <https://mano.is.tue.mpg.de/>.

	## Citation

	```bibtex
	@inproceedings{BenShabat2026RoMoHands,
	author = {Ben-Shabat, Yizhak and Zhang, Jiahao and Liu, Joseph and Moon, Seonghyeon and Lee, Young-Yoon and Jiang, Haomiao and Jacob, Oren and Kapadia, Mubbasir},
	title = {{RoMo Hands}: A Large Scale Richly Organized Text to Hand Motion Dataset},
	booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
	year = {2026},
	}
	```