---
pretty_name: LightGenBench
license: other
license_name: mixed-cc
viewer: false
tags:
- 3d
- pbr
- emission
- texture
- uv-atlas
- voxels
- multiview
size_categories:
- 10K<n<100K
---
<div align="center">
<h1 align="center">LightGenBench: A Benchmark for 3D Emission Generation</h1>

  <a href='#'><img src='https://img.shields.io/badge/Project%20Page-Coming%20Soon-lightgrey'></a> &nbsp;
  <a href='#'><img src='https://img.shields.io/badge/arXiv-Coming%20Soon-lightgrey'></a> &nbsp;
  <a href='#'><img src='https://img.shields.io/badge/GitHub-Coming%20Soon-lightgrey'></a> &nbsp;
  <a href='https://huggingface.co/datasets/3dlg-hcvc/LightgenBench'><img src='https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-LightGenBench-blue'></a>
<br>
<!-- TODO: authors and affiliations, as in the TexVerse card. The paper is an anonymous 3DV submission; fill this in only once naming the authors is fine. -->
</div>

![LightGenBench shapes with their emission on](assets/teaser.jpg)

**LightGenBench is a dataset of emissive 3D objects for training and evaluating emission texture generation.** Its key characteristics:

1. **Scale and source**: 36,826 emissive shapes from [TexVerse](https://huggingface.co/datasets/YiboZhang2001/TexVerse) (Sketchfab models), each with albedo, metallic, roughness, opacity and an emission map. It is curated from the 859k TexVerse models.
2. **Three representations**: every shape comes as a 512×512 UV atlas, 256³ sparse voxels and six orthographic 512×512 views, the inputs of UV-, voxel- and multiview-based texture generators.
3. **Splits**: train 36,426, val 200, test 200 shapes; val and test are category-stratified.

Questions or problems: open a discussion on this repository.<!-- TODO: contact email, if wanted -->

## Download

The repository holds about 117 GB:

```
README.md          this card
splits.json        the split of each shape: {"train": [uuid, ...], "val": [...], "test": [...]}
metadata.parquet   one row per shape: uuid, split, shard, category, license, author, author_username, source_url
checksums.sha256   sha256 of every other file, to verify a download
assets/            the images this card shows
data/              <split>/<kind>/<kind>-<shard>.tar, kind: atlas, voxels, multiview, thumbnail
```

The shapes are sorted based on uuids and cut into shards of 1,000 shapes: train has 37 shards (the
last holds 426), val and test have one for each (200 per shard), 39 shards in all. A shard is stored as four tars: the three representations (UV atlas, O-Voxels, multiview images) and the Sketchfab thumbnail; the four tars of a shard hold the files of the same uuids. For example:

```
data/train/atlas/atlas-00007.tar          <uuid>/atlas.npz
data/train/voxels/voxels-00007.tar        <uuid>/emission_voxels.vxz, <uuid>/pbr_voxels.vxz
data/train/multiview/multiview-00007.tar  <uuid>/multiview/000_albedo.png ... 005_alpha.png, transforms.json
data/train/thumbnail/thumbnail-00007.tar  <uuid>/thumbnail.png
```

`metadata.parquet` gives each shape's `shard`; there are 156 tars in all. Each `atlas.npz` is a
deflate-compressed `.npz`, which `np.load` reads as usual.

```bash
# the root files, then one kind of file for every split
hf download 3dlg-hcvc/LightgenBench README.md splits.json metadata.parquet checksums.sha256 assets/teaser.jpg --repo-type dataset --local-dir lightgenbench
hf download 3dlg-hcvc/LightgenBench --repo-type dataset --include "data/*/voxels/*" --local-dir lightgenbench
# or everything
hf download 3dlg-hcvc/LightgenBench --repo-type dataset --local-dir lightgenbench
# check, then unpack every tar in place: lightgenbench/<uuid>/<file>, next to splits.json
cd lightgenbench && sha256sum -c --ignore-missing checksums.sha256
for t in data/*/*/*.tar; do tar -xf "$t"; done   # rm -r data/ afterwards to free the tar space
```

## Dataset structure

After unpacking, every shape is one directory named by its TexVerse uuid, and `splits.json`
lists the uuids of each split in train/val/test:

```
<uuid>/
  atlas.npz
  emission_voxels.vxz
  pbr_voxels.vxz
  multiview/         00N_{albedo,mr,normal,pos,emission,alpha}.png (N = 0..5), transforms.json
  thumbnail.png
```

### Splits

| split | shapes |
|---|---|
| train | 36,426 |
| val | 200 |
| test | 200 |

Val and test hold 200 shapes each, drawn category-stratified at random; train is every other
released shape.

The validation split picks checkpoints; the test set produces published numbers. Read a split
as `json.load(open("splits.json"))["train"]` and a shape's files as `lightgenbench/<uuid>/<file>`.

### Preprocessing

Every representation is built from the .glb file, normalized to [−1, 1]. Emission is the
material's emissive texture, or its emissive factor as a color when it has no texture.

### Representations

#### `atlas.npz`

The UV atlas of one shape: a single `.npz` holding eight 512×512 maps over the same UV layout, each stored as an array whose dtype and channel count are listed below. `color` and `emission_color` hold linear RGB values.

| key | dtype | shape | content |
|---|---|---|---|
| `occupancy` | bool | 512×512×1 | texel covered by the UV layout |
| `position` | uint16 | 512×512×3 | position in [−1, 1] frame |
| `objnormal` | uint16 | 512×512×3 | object-space normal |
| `color` | uint8 | 512×512×3 | base color |
| `metal` | uint8 | 512×512×1 | metallic |
| `rough` | uint8 | 512×512×1 | roughness |
| `emission_color` | uint8 | 512×512×3 | emission |
| `alpha` | uint8 | 512×512×1 | opacity |

#### `emission_voxels.vxz`, `pbr_voxels.vxz`

Sparse voxels on a 256³ grid over [−0.5, 0.5]³, in the O-Voxel format of
[TRELLIS.2](https://github.com/microsoft/TRELLIS.2), whose `o_voxel` package reads them.
`o_voxel.io.read_vxz(path)` returns: an int32 tensor of shape N×3
holding each stored voxel's grid index (0–255 on each axis), where N is the number of voxels stored
for that shape, and a dict of per-voxel attributes, each a uint8 tensor with one row per voxel. `emissive` and
`base_color` are linear RGB. The two files carry different attributes over the same list of voxels
in the same order. Attributes:

| file | attribute | dtype | shape |
|---|---|---|---|
| `emission_voxels.vxz` | `emissive` | uint8 | N×3 |
| `pbr_voxels.vxz` | `base_color` | uint8 | N×3 |
| | `metallic`, `roughness`, `alpha` | uint8 | N×1 |

#### `multiview/`

Six orthographic 512×512 views (front, left, back, right, top, bottom), rendered with the six fixed
cameras of Hunyuan3D-2.1's
[training example](https://github.com/Tencent-Hunyuan/Hunyuan3D-2.1/blob/main/hy3dpaint/train_examples/001/render_tex/transforms.json),
each with six maps: `albedo`, `mr` (metallic-roughness), `normal`, `pos`,
`emission`, `alpha`. The material maps (`albedo`, `mr`, `emission`, `alpha`) hold linear bytes;
`normal` and `pos` are geometry maps.
The object mask is `mr` red channel == 255.
`transforms.json` holds the six camera frames.

### `thumbnail.png`

The TexVerse preview images: 36,824 hold JPEG data and 2 hold
PNG data. 35,304 are 1920×1080 RGB; the other 1,522 are smaller, down to
256×144, and 277 of those are grayscale.

## License

Every shape keeps the license of its source model on Sketchfab (via TexVerse); `metadata.parquet`
gives each shape's `license`, `author` and `source_url`. Credit the authors and filter on the
`license` column for your use: NonCommercial licenses allow non-commercial use only, ShareAlike
licenses require the same license on derived work, and NoDerivs licenses do not allow sharing
adapted material.

- [CC BY](https://creativecommons.org/licenses/by/4.0/): 34,319
- [CC BY-NC](https://creativecommons.org/licenses/by-nc/4.0/): 1,341
- [CC BY-NC-SA](https://creativecommons.org/licenses/by-nc-sa/4.0/): 459
- [CC BY-NC-ND](https://creativecommons.org/licenses/by-nc-nd/4.0/): 381
- [CC BY-SA](https://creativecommons.org/licenses/by-sa/4.0/): 264
- [CC BY-ND](https://creativecommons.org/licenses/by-nd/4.0/): 49
- [CC0](https://creativecommons.org/publicdomain/zero/1.0/): 13

## BibTeX

<!-- TODO: fill in once the paper is public. -->
```
@inproceedings{lightgenbench2026,
  title  = {LightGenBench: A Benchmark for 3D Emission Generation},
  author = {},
  year   = {2026}
}
```