---
pretty_name: VisSlideBench
license: cc-by-nc-4.0
language:
- en
task_categories:
- text-to-image
- image-to-image
tags:
- slides
- presentation
- text-rendering
- image-editing
- benchmark
size_categories:
- 1K<n<10K
configs:
- config_name: t2i
  data_files:
  - split: test
    path: data/t2i.jsonl
- config_name: template
  data_files:
  - split: test
    path: data/template.jsonl
- config_name: multi_image
  data_files:
  - split: test
    path: data/multi_image.jsonl
- config_name: edit
  data_files:
  - split: test
    path: data/edit.jsonl
---

# VisSlideBench

VisSlideBench evaluates single-slide image generation and editing. It has 1,527
manually screened cases in four tracks that share one text-and-image interface.

[📄 Paper](https://arxiv.org/abs/2610.16784) | [💻 Code](https://github.com/VectorSpaceLab/InfoVis) | [🤗 Model](https://huggingface.co/InfoVis/InfoVis) | [📦 Training data](https://huggingface.co/datasets/InfoVis/Data)

| Track | Config | Visual input | Real | Synthetic | Total |
|---|---|---|---:|---:|---:|
| Text-to-slide | `t2i` | none | 167 | 150 | 317 |
| Template-conditioned | `template` | one reference slide | 147 | 150 | 297 |
| Multi-image grounded | `multi_image` | one to four assets | 179 | 150 | 329 |
| Slide editing | `edit` | source slide | – | – | 584 |

Real cases come from presentation decks. Synthetic cases were built with
Nano-Banana-2. Every generation track mixes three prompt forms: short requests,
detailed descriptions, and structured `style_json` specifications.

| Prompt form | t2i | template | multi_image |
|---|---:|---:|---:|
| short | 115 | 111 | 103 |
| long | 109 | 114 | 110 |
| style_json | 93 | 72 | 116 |

The edit track covers nine operations: text modification (50), block addition
(74), block removal (49), background update (52), color change (49), font change
(49), typography change (73), image replacement (103), and theme switching (85).

## Files

```
data/t2i.jsonl  data/template.jsonl  data/multi_image.jsonl  data/edit.jsonl
images/<track>/<id>_input_<k>.<ext>   # visual inputs (template, multi_image)
images/<track>/<id>_target.<ext>      # reference target slide, when available
images/edit/<id>_source.png           # slide to edit
images/edit/<id>_target.png           # reference edited slide
eval/                                 # scoring code and judge prompts
```

All image paths in the jsonl files are relative to the dataset root.

Generation record:

```json
{"index": 0, "id": "46f1aa2e-...", "task": "t2i", "source": "real", "prompt_type": "short",
 "instruction": "The slide is titled ...", "input_images": [],
 "target_image": "images/t2i/46f1aa2e-..._target.jpg", "width": 2589, "height": 1455}
```

`width` and `height` are the size of the reference slide. Our runs generate
t2i cases at 1280x720 and template and multi-image cases at 1376x768.
`target_image` is a reference only and is `null` for some synthetic
template and multi-image cases; no metric compares against it.

Edit record:

```json
{"index": 0, "id": "add_block__add_000299", "task": "edit", "operation": "add_block",
 "instruction": "Insert the subtitle ...", "source_image": "images/edit/..._source.png",
 "target_image": "images/edit/..._target.png", "width": 1280, "height": 720}
```

## Evaluation

Save one PNG per case as `<pred_dir>/<track>/<id>.png`, then run the scripts in
`eval/` (see `eval/README.md`).

| Track | Metrics |
|---|---|
| t2i, template | Content Faithfulness (CF), Instruction Following (IF), Aesthetic Quality (AQ) |
| multi_image | CF (text and input-image preservation, averaged), IF, AQ |
| edit | Semantic Consistency (SC = min of edit success and edit consistency), Presentation Quality (PQ) |

CF text fidelity uses OCR and a normalized edit distance against the text the
instruction requires. IF, AQ, image preservation, SC, and PQ use GPT-4.1 as the
judge; IF and AQ are the unweighted means of their sub-dimension scores. The
overall score averages the eleven metrics.

## License

VisSlideBench is released under CC BY-NC 4.0 for non-commercial research use.
Real cases come from publicly shared presentation decks, whose content remains
the property of its authors; synthetic cases were generated with Nano-Banana-2.
If you own content in this dataset and want it removed, please open a
discussion on this repository.

## Citation

```bibtex
@article{wang2026infovis,
  title={InfoVis: Unified Slide Image Generation and Editing},
  author={Jiahao Wang and Xiyan Jiang and Chenyuan Wu and Haoqiang Zhang and Xin Luo and Yueze Wang and Defu Lian and Bo Zhang and Jiajun Zhang and Zheng Liu},
  journal={arXiv preprint arXiv:2610.16784},
  year={2026}
}
```