| --- |
| 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](https://arxiv.org/abs/2605.15764) · [🌐 Project Page](https://social-reaoning.github.io/grasp/) · [💻 Code](https://github.com/Social-Reaoning/grasp) · [🤖 Model](https://huggingface.co/interlive/GRASP-Qwen3-VL-8B) |
|
|
| 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: |
|
|
| ```json |
| [{"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: |
|
|
| ```bash |
| 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](https://creativecommons.org/licenses/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 |
|
|
| ```bibtex |
| @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} |
| } |
| ``` |
|
|
