Datasets:
license: cc-by-nc-4.0
task_categories:
- video-text-to-text
language:
- en
tags:
- social-reasoning
- gaze
- gesture
- video-question-answering
size_categories:
- 100K<n<1M
configs:
- config_name: default
data_files:
- split: train
path: train/annotations.jsonl
- split: test
path: test/annotations.jsonl
GRASP: Learning to Ground Social Reasoning in Multi-Person Non-Verbal Interactions
📜 Paper · 🌐 Project Page · 💻 Code · 🤖 Model
GRASP is a large-scale social reasoning dataset that connects high-level social QA with fine-grained gaze and deictic gesture events, organized by a 16-category taxonomy spanning gaze (T1–T6), gesture (G1–G6), and joint gaze–gesture reasoning (J1–J4), together with GRASP-Bench for evaluation.
Structure
train/
├── annotations.jsonl # all training QA, one row per QA (shown in the Dataset Viewer)
├── data_sft.jsonl # open-ended QA with structured reasoning traces (SFT, training format)
├── data_rl.jsonl # MCQ with ground-truth social events (SGR / GRPO, training format)
└── videos/ # per-QA video clips, tar parts (<=9GB each)
├── avsbench_part001.tar # extracts to avsbench/video/*.mp4
├── embody3d_part001.tar ...
└── ...
test/
├── annotations.jsonl # GRASP-Bench QA, one row per QA (shown in the Dataset Viewer)
└── grasp_bench.tar # GRASP-Bench: json/*.json + video/*.mp4 (1,196 items)
annotations.jsonl columns: id, video (path inside the extracted archives), source, category, difficulty, format (mcq / open_ended), question, options, answer, response (structured reasoning target for open-ended training QA).
Each training JSONL line is a list of input specs:
[{"type": "video", "path": "avsbench/video/<clip>.mp4", "fps": 2.0, ...},
{"type": "text", "content": "<question>", "output": false},
{"type": "text", "content": "<answer>", "output": true}]
Video path fields are relative to the extracted train/videos/ root. Extract all tars into one directory and point training at it:
for t in train/videos/*.tar; do tar -xf "$t" -C /path/to/video_root; done
GRASP-Bench items are one JSON per QA with clip_file, qa.category (T1–T6 / G1–G6 / C1–C4 for joint), qa.question, qa.options, qa.answer.
Usage
Training and evaluation code: https://github.com/Social-Reaoning/grasp
License
GRASP annotations (QA pairs and social event labels) are released under CC BY-NC 4.0 for non-commercial research use only. Video clips are derived from AVSBench, Embody3D, FriendsMMC, Social-IQ, SocialGesture, TVQA, and Werewolf Among Us, and remain subject to the licenses and terms of use of their original sources. Commercial use is not permitted.
Citation
@article{kim2026grasp,
title={GRASP: Learning to Ground Social Reasoning in Multi-Person Non-Verbal Interactions},
author={Kim, Junho and Cao, Xu and Yang, Houze and Boote, Bikram and Jojic, Ana and Ryan, Fiona and Lai, Bolin and Lee, Sangmin and Rehg, James M},
journal={arXiv preprint arXiv:2605.15764},
year={2026}
}