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 | 💻 Code | 🤗 Model | 📦 Training 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:

{"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:

{"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

@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}
}