---
license: mit
task_categories:
  - robotics
  - graph-ml
tags:
  - cloth
  - simulation
  - mesh
  - graph-neural-network
  - point-cloud
size_categories:
  - n<1K
---

# Cloth-splatters/lifting-meshes

Synthetic cloth-lifting trajectories for training graph-based dynamics
models. The action sampler picks a single vertex and lifts it vertically
while the rest of the cloth deforms under gravity.

Currently only the no-hole cloth subset is published; with-hole variants
may be added later.

Used by:

- [`Cloth-splatters/lifting-dynamics-gns`](https://huggingface.co/Cloth-splatters/lifting-dynamics-gns)
- [`Cloth-splatters/lifting-dynamics-edge-gnn`](https://huggingface.co/Cloth-splatters/lifting-dynamics-edge-gnn)

## Files

| File | Size | Train / Val cloths | Cameras |
|---|---|---|---|
| `lift_seed_338.h5` | 540 MB | 30 / 15 | `cam_0` |
| `lift_seed_6438.h5` | 524 MB | — | `cam_0` |

Cloth names are of the form `cloth_without_hole_<id>`.

## Schema

Mostly identical to `folding-meshes`, with one difference: lifting files do
**not** carry per-cloth `rest_positions` — only `edges` and `faces`. Use
`step_0000/positions` as the initial flat-cloth state if you need a rest
configuration.

```
metadata/rendering_parameters/...
training/
  <cloth_name>/
    edges  (E, 2) int32
    faces  (F, 3) int32
    trajectory_<i>/
      actuated_vertices (V, 1) float64
      goal_vertices     (V, 1) float64
      step_<n>/
        positions    (V, 3) float64
        velocities   (V, 3) float64
        gripper_pos  (1, 3) float64
        pointclouds/cam_0  (N, 3)
        images/cam_0       (H, W, 3)
        depth/cam_0        (H, W)
        segmentation/cam_0 (H, W)
validation/...
```

## Loading

```python
from huggingface_hub import hf_hub_download
path = hf_hub_download(
    "Cloth-splatters/lifting-meshes",
    filename="lift_seed_338.h5",
    repo_type="dataset",
)
```

## License

MIT.