---
license: apache-2.0
language:
- en
task_categories:
- image-to-3d
- text-to-3d
- other
tags:
- 3d
- 3d-editing
- mesh
- shape-vae
- cube3d
- webdataset
size_categories:
- 1M<n<10M
pretty_name: 3DEdit-1M
configs:
- config_name: default
  data_files:
  - split: train
    path: "data/shard-*.tar"
---

<h1 align="center" style="font-size: 3em; margin-bottom: 0.2em;">3DEdit-1M</h1>

A large-scale 3D edit-pair dataset of the paper
**[Omni123: Exploring 3D Native Foundation Models with Limited 3D Data by Unifying Text to 2D and 3D Generation](https://arxiv.org/abs/2604.02289)**.

| | |
| --- | --- |
| **Forward edit pairs** | **1,476,827** (487,928 distinct source objects) |
| **Reverse edit instructions** | **1,356,826** (91.9% of the pairs) |
| Shards | 211 WebDataset tars, `data/shard-{000136..000346}.tar` |
| Size | 309.7 GB |

Each sample is a paired (source, target) 3D edit. The pipeline behind every sample:

- **Edit instruction** synthesized by [Qwen3.5-35B-A3B](https://huggingface.co/Qwen/Qwen3.5-35B-A3B).
- **Target image** (post-edit RGB render) generated by [FLUX.2-klein-9b-kv](https://huggingface.co/black-forest-labs/FLUX.2-klein-9b-kv).
- **Source and target 3D meshes** generated by [Hunyuan3D-2-mini](https://github.com/Tencent-Hunyuan/Hunyuan3D-2) from the source and target images.
- **Shape tokenization**: each mesh is tokenized with the [cube3d v0.5](https://huggingface.co/Roblox/cube3d-v0.5) shape encoder (`OneDAutoEncoder`); the released `.npy` files store those discrete encoder indices.

## Dataset structure

```
data/
├── shard-000136.tar
├── shard-000137.tar
├── ...
└── shard-000346.tar          # 211 shards, 7,000 samples each (last one 6,827)
reverse_instructions.jsonl    # reverse edit instructions, keyed by sample id
```

Sample keys are `<uuid>_<k>`: a single source object (`uuid`) yields up to 4 different
edits, indexed by `k`.

| File | Description |
| --- | --- |
| `<id>/<id>.source.npy` | Source shape token indices from the [cube3d v0.5](https://huggingface.co/Roblox/cube3d-v0.5) encoder — shape `(1, 1024)` `int64`, codebook size 16,384 (value range `[0, 16383]`) |
| `<id>/<id>.target.npy` | Target shape token indices — same encoder, same shape/dtype |
| `<id>/<id>.source.rgba.webp` | Source RGBA render, 1024×1024, lossless WEBP |
| `<id>/<id>.target.rgba.webp` | Target RGBA render, 512×512, lossless WEBP |
| `<id>/<id>.meta.json` | Edit instruction and metadata (see below) |

### `meta.json` fields

```json
{
  "id": "b734f7f2-62f3-4397-882c-98f9387f66bc_1",
  "uuid": "b734f7f2-62f3-4397-882c-98f9387f66bc",
  "captions": ["long caption ...", "medium caption ...", "short caption"],
  "instruction": "Replace the current side mirror with a smaller, more angular, and aerodynamically shaped mirror."
}
```

`captions` describe the **source** (pre-edit) object at three levels of detail.
`instruction` is the edit that was actually applied to produce this sample's target.

**Loading with WebDataset**

```python
import webdataset as wds

url = "https://huggingface.co/datasets/meshy-ai-team/3DEdit-1M/resolve/main/data/shard-{000136..000346}.tar"
ds = (
    wds.WebDataset(url, shardshuffle=True)
    .decode("rgb")
    .to_tuple("source.npy", "target.npy",
              "source.rgba.webp",
              "target.rgba.webp",
              "meta.json")
)
```

## Reverse edit instructions

The shards contain **forward** edits only (source → target). `reverse_instructions.jsonl`
supplies the inverse instruction for each sample, so a reverse pair can be built by
**swapping source and target** and using the reverse instruction in place of
`meta.json["instruction"]`. Together the two directions give ~2.83M edit pairs.

The reverse instructions were generated by [Qwen3.6-27B](https://huggingface.co/Qwen/Qwen3.6-27B)
(thinking mode, dual-image): the model is shown the edited object and the original object
together with the forward instruction, proposes candidate inverse instructions, filters out
the ones that are colour-only, scale-only or vague, and selects the best. Grounding the
model in both images lets the inverse name concrete properties of the original part that
the forward instruction never mentions:

```
forward : Replace the tail rotor assembly with a larger, more angular multi-blade tail rotor.
reverse : Replace the large multi-blade tail rotor with a smaller two-bladed tail rotor assembly.
```

One JSON object per line:

```json
{"id": "0000661e-710b-4e07-9ec4-571531a94804_0",
 "uuid": "0000661e-710b-4e07-9ec4-571531a94804",
 "instruction": "Replace the large multi-blade tail rotor with a smaller two-bladed tail rotor assembly.",
 "fallback": false}
```

| Field | Description |
| --- | --- |
| `id` | Matches the sample key inside the shards — join on this |
| `uuid` | Source object id (the `id` without the `_<k>` suffix) |
| `instruction` | The reverse edit instruction |
| `fallback` | `true` when no candidate survived filtering and a best-effort pick was used (117,503 rows, 8.66%). Lower quality — filter these out if you need a clean subset. |

Notes on coverage:

- 1,356,826 of the 1,476,827 samples have a reverse instruction (**91.9%**). The remaining
  120,001 were never generated or were dropped as degenerate outputs; join on `id` and skip
  misses rather than assuming full coverage.
- Instruction length ranges from 20 to 497 characters (median 69).

## License

Released under the **[Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0)**.