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license: mit
pretty_name: AgentViSS
language:
- en
size_categories:
- n<1K
tags:
- multimodal
- social-simulation
- visual-social-intelligence
- multi-agent-simulation
configs:
- config_name: default
data_files:
- split: train
path: train.jsonl
---
# AgentViSS Dataset
This repository contains the public data package for **AgentViSS: Can Agents
Read the Room? Benchmarking Visual Social Intelligence in Multimodal
Simulation**.
## Files
```text
.
+-- README.md
+-- train.jsonl
+-- data.json
+-- images/
+-- scenario_001/
| +-- group_labeled.png
| +-- individual/
| +-- character_01.jpg
| +-- character_02.jpg
+-- ...
```
- `train.jsonl` is a viewer-friendly table with 240 AgentViSS records. It
exposes key fields from `data.json` and keeps image path columns at the end.
- `data.json` contains the canonical full records.
- `images/` contains all image assets referenced by `data.json`.
- Image paths in `data.json` are package-relative. Resolve them from the
dataset repository root.
- All images are resized proportionally so that the longest edge is at most
512 pixels.
## Dataset Structure
Each record includes an anonymized scenario id, dialogue type, conflict level,
characters, role-level information, goals, emotions, and image paths.
The Hugging Face Dataset Viewer is configured to read `train.jsonl` rather than
the raw `images/` directory. This keeps the viewer organized around the 240
scenario records instead of displaying the 282 image files as separate rows.
Important image fields:
- `group_image_labeled`: path to the scenario-level group image.
- `individual_images`: mapping from character names to role portrait paths.
Example:
```json
{
"id": "agentviss_low_01",
"source_scenario": "scenario_001",
"group_image_labeled": "images/scenario_001/group_labeled.png",
"individual_images": {
"CharacterName": "images/scenario_001/individual/character_01.jpg"
}
}
```
The fields `base_json_path`, `group_image`, `background`, and
`individual_informations.*.personality` are not included in this public data
package.
## Download
Download the full dataset, including images, with `huggingface_hub`:
```python
import json
from pathlib import Path
from huggingface_hub import snapshot_download
root = Path(snapshot_download(
repo_id="JunsWan/AgentViSS",
repo_type="dataset",
))
records = json.loads((root / "data.json").read_text(encoding="utf-8"))
first_group_image = root / records[0]["group_image_labeled"]
```
For the flattened viewer table:
```python
import json
from pathlib import Path
from huggingface_hub import snapshot_download
root = Path(snapshot_download(
repo_id="JunsWan/AgentViSS",
repo_type="dataset",
))
with (root / "train.jsonl").open(encoding="utf-8") as f:
rows = [json.loads(line) for line in f]
```
You can also clone the repository:
```bash
git clone https://huggingface.co/datasets/JunsWan/AgentViSS
```
|