Datasets:

Enderfga
/

SolarRenderer-Data

---
license: other
license_name: per-source
pretty_name: SolarRenderer Data
task_categories:
- video-to-video
- text-to-video
tags:
- neural-rendering
- game-engine
- white-box
- controllable-video-generation
- webdataset
size_categories:
- 10K<n<100K
---
<p align="center">
  <img src="https://huggingface.co/datasets/Enderfga/SolarRenderer-Data/resolve/main/assets/logo.png" width="160"/>
</p>

<h1 align="center">SolarRenderer: Few-Step Generative Reference Rendering</h1>

<p align="center">
  🌐 <a href="https://enderfga.cn/SolarRenderer">Project Page</a> •
  🎮 <a href="https://huggingface.co/spaces/Enderfga/SolarRenderer">Online Demo</a> •
  📄 <a href="https://arxiv.org/abs/2610.28630">Paper</a> •
  🤗 <a href="https://huggingface.co/Enderfga/SolarRenderer">Weights</a> •
  💻 <a href="https://github.com/Enderfga/SolarRenderer">Code</a> •
  🛠️ <a href="https://github.com/Enderfga/SolarRenderer/tree/data">Data code</a>
</p>

Training data and benchmark of **SolarRenderer**, which renders structure from game engines and agents (grey boxes,
semantic maps, masks and skeletons, and structure types it never saw in training) into finished video.

<p align="center">
  <img src="https://huggingface.co/datasets/Enderfga/SolarRenderer-Data/resolve/main/assets/data_overview.jpg" width="100%"/>
</p>

## Contents

| Part | Size | What is in it |
|---|---|---|
| `train/magpie` | 30,597 clips | render, scene grey box, first frame, prompt |
| `train/carla` | 25,317 clips | render, semantic segmentation, first frame, prompt |
| `train/unrealzoo` | 1,824 clips | render, object masks + skeletons, first frame, prompt |
| `benchmark/hard` | 208 cases | structure, first frame, prompt, tags (16 structure types, 11 unseen in training) |
| `benchmark/heldout` | 53 clips | structure, real first frame, prompt, ground truth |
| `results/` | 522 renders, 44 files of verdicts | renders of both benchmark splits by SolarRenderer and its causal variant, and the judge verdicts behind the paper's Elo tables |

📌 Of the training sources, the Magpie, CARLA and UnrealZoo clips are released; held-out test clips of the engine
recordings with near-range depth are in `benchmark/heldout`.

## Quick Start

```python
import webdataset as wds
from huggingface_hub import HfFileSystem
base = "https://huggingface.co/datasets/Enderfga/SolarRenderer-Data/resolve/main/"
urls = [base + p.split("/", 3)[3] for p in HfFileSystem().glob("datasets/Enderfga/SolarRenderer-Data/train/magpie/*.tar")]
for clip in wds.WebDataset(urls).decode("pil"):   # first_frame.png -> PIL image, the videos stay mp4 bytes
    render, condition, first_frame, meta = clip["render.mp4"], clip["condition.mp4"], clip["first_frame.png"], clip["json"]
    break
```

Benchmark only: `hf download Enderfga/SolarRenderer-Data --repo-type dataset --include "benchmark/*"`; our renders and
the judge verdicts: `--include "results/*"`. Scoring code: [`eval/`](https://github.com/Enderfga/SolarRenderer/tree/main/eval) in the `main` branch.
To train on the shards, [`tools/hf_samples.py`](https://github.com/Enderfga/SolarRenderer/blob/main/tools/hf_samples.py)
turns them into training samples ([guide](https://github.com/Enderfga/SolarRenderer/blob/main/docs/training.md)).

<details><summary><b>Format</b></summary>

- Training clips are exactly what the model saw: 24 fps, the first 158 frames, LANCZOS to 1344×768, the structure cut
  the same way. H.264, CRF 18 (render) / 15 (structure).
- Held-out clips (24 fps, 158 frames, 1344×768) are the very files the paper's held-out numbers were computed from, so
  `eval/` on `results/solarrenderer` reproduces them.
- Training shards are WebDataset tars of about 2 GB; one clip = `<key>.render.mp4`, `<key>.condition.mp4`,
  `<key>.first_frame.png`, `<key>.json`. `train/index.jsonl` lists every clip with its shard.
- `training_input` in the index says which inputs training gave the clip; the prompt is written for that combination.

```
train/index.jsonl, train/<source>/<source>-00000.tar ...
benchmark/hard/cases.jsonl, benchmark/hard/<case>/{condition.mp4, first_frame.png, prompt.txt}
benchmark/heldout/cases.jsonl, benchmark/heldout/clips/<source>/<clip>/{render.mp4, condition.mp4, first_frame.png}
results/solarrenderer/{hard/<case>.mp4, heldout/<source>/<clip>.mp4}
results/solarrenderer_causal/...      same layout, the causal variant
results/judge/{hard,heldout}/<method>__vs__<method>.jsonl
page/                                 videos of the project page
```
</details>

<details><summary><b>Prompts</b></summary>

- Six parts: `subject_definitions`, `summary`, `retention_analysis`, `detailed_description` (350–500 words),
  `overall_soundscape`, `non_diegetic_music`.
- The parts that name the inputs are fixed sentences per structure type; the rest was written per clip by Kimi K2.6.
- Captioning pipeline: [`data` branch](https://github.com/Enderfga/SolarRenderer/tree/data). Prompt-writing skill: [`skills/solarrenderer-caption/`](https://github.com/Enderfga/SolarRenderer/tree/main/skills/solarrenderer-caption) in the `main` branch.
</details>

<details><summary><b>Benchmark</b></summary>

- `hard`: 57 structure clips, each in several looks. 74 cases have a structure rendering not seen in training
  (`condition_seen_in_training: false`), including the DirtyMoCap SMPL renders with markers.
- Structures: mostly built by our agent in a game engine; 49 near-field shaded geometry cases from Depth Anything 3
  depth of MiniMax-H3 clips; CARLA semantics, fitted SMPL, a Mixamo character, and the DirtyMoCap project video as is.
- First frames: Nano Banana redraws of the structure's frame 0 in each case's look.
- `heldout`: 53 clips from CARLA (14), engine recordings with near-range depth (9), Magpie (16) and UnrealZoo (14), held out by recording, scene or map
  (CARLA by clip, so its maps also appear in training). Each gives the structure video, the real first frame and the
  prompt; `render.mp4` is the ground truth.
- `results/solarrenderer`: the SolarRenderer renders scored in the paper (four steps, seed 0).
- `results/solarrenderer_causal`: the renders of the causal LoRA pair, streamed block by block (four steps per block,
  seed 0), scored in the paper as Ours (causal).
- `results/judge`: pairwise verdicts in the format of `eval/judge.py`, among the main table's nine methods and the
  four-step LoRAs we started from (LongLive-Plug, DMAD, FastH3 with our control LoRA). `eval/elo.py --methods` with
  the nine methods gives the main table's Elo. The causal variant is judged only against SolarRenderer
  (`solarrenderer__vs__solarrenderer_causal.jsonl`); adding it as a tenth method and shifting the ratings so that the
  other nine average 1000 gives its row.
</details>

## License

| Part | License |
|---|---|
| Magpie clips | Apache-2.0, from [Magpie](https://huggingface.co/datasets/MogoAI/Magpie_lite) |
| CARLA clips | CC BY 4.0; rendered by us in [CARLA](https://carla.org) 0.9.16 (code MIT, assets CC-BY) |
| UnrealZoo clips | CC BY-NC 4.0; rendered by us in [UnrealZoo](https://unrealzoo.github.io) (Apache-2.0) |
| Benchmark | ours, except inputs noted per case; held-out engine-recording clips for research use only |
| `page/cmp` | excerpts of the published demos of the 15 works compared on the project page, shown for comparison; each remains under its authors' terms |
| Other prompts, indexes, metadata | CC BY 4.0 |

Code: Apache-2.0. Weights: MiniMax H3 Community License (see the
[weights repository](https://huggingface.co/Enderfga/SolarRenderer)).

📮 Questions or removal requests: guianfang@u.nus.edu

## Citation

```bibtex
@misc{fang2026solarrenderer,
  title  = {SolarRenderer: Few-Step Generative Reference Rendering},
  author = {Fang, Guian and Qian, Shengju and Lu, Dongyue and
            Huang, Junchao and Jiang, Lutao and Wang, Xin and
            Song, Yiren and Jiang, Li and Shou, Mike Zheng},
  year   = {2026},
  eprint = {2610.28630},
  archivePrefix = {arXiv}
}
```

	---
	license: other
	license_name: per-source
	pretty_name: SolarRenderer Data
	task_categories:
	- video-to-video
	- text-to-video
	tags:
	- neural-rendering
	- game-engine
	- white-box
	- controllable-video-generation
	- webdataset
	size_categories:
	- 10K<n<100K
	---

	<p align="center">
	<img src="https://huggingface.co/datasets/Enderfga/SolarRenderer-Data/resolve/main/assets/logo.png" width="160"/>
	</p>

	<h1 align="center">SolarRenderer: Few-Step Generative Reference Rendering</h1>

	<p align="center">
	🌐 <a href="https://enderfga.cn/SolarRenderer">Project Page</a> •
	🎮 <a href="https://huggingface.co/spaces/Enderfga/SolarRenderer">Online Demo</a> •
	📄 <a href="https://arxiv.org/abs/2610.28630">Paper</a> •
	🤗 <a href="https://huggingface.co/Enderfga/SolarRenderer">Weights</a> •
	💻 <a href="https://github.com/Enderfga/SolarRenderer">Code</a> •
	🛠️ <a href="https://github.com/Enderfga/SolarRenderer/tree/data">Data code</a>
	</p>

	Training data and benchmark of SolarRenderer, which renders structure from game engines and agents (grey boxes,
	semantic maps, masks and skeletons, and structure types it never saw in training) into finished video.

	<p align="center">
	<img src="https://huggingface.co/datasets/Enderfga/SolarRenderer-Data/resolve/main/assets/data_overview.jpg" width="100%"/>
	</p>

	## Contents

	\| Part \| Size \| What is in it \|
	\|---\|---\|---\|
	\| `train/magpie` \| 30,597 clips \| render, scene grey box, first frame, prompt \|
	\| `train/carla` \| 25,317 clips \| render, semantic segmentation, first frame, prompt \|
	\| `train/unrealzoo` \| 1,824 clips \| render, object masks + skeletons, first frame, prompt \|
	\| `benchmark/hard` \| 208 cases \| structure, first frame, prompt, tags (16 structure types, 11 unseen in training) \|
	\| `benchmark/heldout` \| 53 clips \| structure, real first frame, prompt, ground truth \|
	\| `results/` \| 522 renders, 44 files of verdicts \| renders of both benchmark splits by SolarRenderer and its causal variant, and the judge verdicts behind the paper's Elo tables \|

	📌 Of the training sources, the Magpie, CARLA and UnrealZoo clips are released; held-out test clips of the engine
	recordings with near-range depth are in `benchmark/heldout`.

	## Quick Start

	```python
	import webdataset as wds
	from huggingface_hub import HfFileSystem

	base = "https://huggingface.co/datasets/Enderfga/SolarRenderer-Data/resolve/main/"
	urls = [base + p.split("/", 3)[3] for p in HfFileSystem().glob("datasets/Enderfga/SolarRenderer-Data/train/magpie/*.tar")]
	for clip in wds.WebDataset(urls).decode("pil"): # first_frame.png -> PIL image, the videos stay mp4 bytes
	render, condition, first_frame, meta = clip["render.mp4"], clip["condition.mp4"], clip["first_frame.png"], clip["json"]
	break
	```

	Benchmark only: `hf download Enderfga/SolarRenderer-Data --repo-type dataset --include "benchmark/*"`; our renders and
	the judge verdicts: `--include "results/*"`. Scoring code: [`eval/`](https://github.com/Enderfga/SolarRenderer/tree/main/eval) in the `main` branch.
	To train on the shards, [`tools/hf_samples.py`](https://github.com/Enderfga/SolarRenderer/blob/main/tools/hf_samples.py)
	turns them into training samples ([guide](https://github.com/Enderfga/SolarRenderer/blob/main/docs/training.md)).

	<details><summary><b>Format</b></summary>

	- Training clips are exactly what the model saw: 24 fps, the first 158 frames, LANCZOS to 1344×768, the structure cut
	the same way. H.264, CRF 18 (render) / 15 (structure).
	- Held-out clips (24 fps, 158 frames, 1344×768) are the very files the paper's held-out numbers were computed from, so
	`eval/` on `results/solarrenderer` reproduces them.
	- Training shards are WebDataset tars of about 2 GB; one clip = `<key>.render.mp4`, `<key>.condition.mp4`,
	`<key>.first_frame.png`, `<key>.json`. `train/index.jsonl` lists every clip with its shard.
	- `training_input` in the index says which inputs training gave the clip; the prompt is written for that combination.

	```
	train/index.jsonl, train/<source>/<source>-00000.tar ...
	benchmark/hard/cases.jsonl, benchmark/hard/<case>/{condition.mp4, first_frame.png, prompt.txt}
	benchmark/heldout/cases.jsonl, benchmark/heldout/clips/<source>/<clip>/{render.mp4, condition.mp4, first_frame.png}
	results/solarrenderer/{hard/<case>.mp4, heldout/<source>/<clip>.mp4}
	results/solarrenderer_causal/... same layout, the causal variant
	results/judge/{hard,heldout}/<method>__vs__<method>.jsonl
	page/ videos of the project page
	```
	</details>

	<details><summary><b>Prompts</b></summary>

	- Six parts: `subject_definitions`, `summary`, `retention_analysis`, `detailed_description` (350–500 words),
	`overall_soundscape`, `non_diegetic_music`.
	- The parts that name the inputs are fixed sentences per structure type; the rest was written per clip by Kimi K2.6.
	- Captioning pipeline: [`data` branch](https://github.com/Enderfga/SolarRenderer/tree/data). Prompt-writing skill: [`skills/solarrenderer-caption/`](https://github.com/Enderfga/SolarRenderer/tree/main/skills/solarrenderer-caption) in the `main` branch.
	</details>

	<details><summary><b>Benchmark</b></summary>

	- `hard`: 57 structure clips, each in several looks. 74 cases have a structure rendering not seen in training
	(`condition_seen_in_training: false`), including the DirtyMoCap SMPL renders with markers.
	- Structures: mostly built by our agent in a game engine; 49 near-field shaded geometry cases from Depth Anything 3
	depth of MiniMax-H3 clips; CARLA semantics, fitted SMPL, a Mixamo character, and the DirtyMoCap project video as is.
	- First frames: Nano Banana redraws of the structure's frame 0 in each case's look.
	- `heldout`: 53 clips from CARLA (14), engine recordings with near-range depth (9), Magpie (16) and UnrealZoo (14), held out by recording, scene or map
	(CARLA by clip, so its maps also appear in training). Each gives the structure video, the real first frame and the
	prompt; `render.mp4` is the ground truth.
	- `results/solarrenderer`: the SolarRenderer renders scored in the paper (four steps, seed 0).
	- `results/solarrenderer_causal`: the renders of the causal LoRA pair, streamed block by block (four steps per block,
	seed 0), scored in the paper as Ours (causal).
	- `results/judge`: pairwise verdicts in the format of `eval/judge.py`, among the main table's nine methods and the
	four-step LoRAs we started from (LongLive-Plug, DMAD, FastH3 with our control LoRA). `eval/elo.py --methods` with
	the nine methods gives the main table's Elo. The causal variant is judged only against SolarRenderer
	(`solarrenderer__vs__solarrenderer_causal.jsonl`); adding it as a tenth method and shifting the ratings so that the
	other nine average 1000 gives its row.
	</details>

	## License

	\| Part \| License \|
	\|---\|---\|
	\| Magpie clips \| Apache-2.0, from [Magpie](https://huggingface.co/datasets/MogoAI/Magpie_lite) \|
	\| CARLA clips \| CC BY 4.0; rendered by us in [CARLA](https://carla.org) 0.9.16 (code MIT, assets CC-BY) \|
	\| UnrealZoo clips \| CC BY-NC 4.0; rendered by us in [UnrealZoo](https://unrealzoo.github.io) (Apache-2.0) \|
	\| Benchmark \| ours, except inputs noted per case; held-out engine-recording clips for research use only \|
	\| `page/cmp` \| excerpts of the published demos of the 15 works compared on the project page, shown for comparison; each remains under its authors' terms \|
	\| Other prompts, indexes, metadata \| CC BY 4.0 \|

	Code: Apache-2.0. Weights: MiniMax H3 Community License (see the
	[weights repository](https://huggingface.co/Enderfga/SolarRenderer)).

	📮 Questions or removal requests: guianfang@u.nus.edu

	## Citation

	```bibtex
	@misc{fang2026solarrenderer,
	title = {SolarRenderer: Few-Step Generative Reference Rendering},
	author = {Fang, Guian and Qian, Shengju and Lu, Dongyue and
	Huang, Junchao and Jiang, Lutao and Wang, Xin and
	Song, Yiren and Jiang, Li and Shou, Mike Zheng},
	year = {2026},
	eprint = {2610.28630},
	archivePrefix = {arXiv}
	}
	```