AgentViSS Dataset

This repository contains the public data package for AgentViSS: Can Agents Read the Room? Benchmarking Visual Social Intelligence in Multimodal Simulation.

Files

.
+-- 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:

{
  "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:

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:

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:

git clone https://huggingface.co/datasets/JunsWan/AgentViSS