| title | GRASP: Learning to Ground Social Reasoning in Multi-Person Non-Verbal Interactions |
|---|---|
| authors | Junho Kim, Xu Cao, Houze Yang, Bikram Boote, Ana Jojic, Fiona Ryan, Bolin Lai, Sangmin Lee, James M. Rehg |
| date | 2026-05-15 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2605.15764 |
| affiliation | University of Illinois at Urbana-Champaign |
| upvotes | 3 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf |
| abstract | Understanding social interactions requires reasoning over subtle non-verbal cues, yet current multimodal large language models (MLLMs) often fail to identify who interacts with whom in multi-person videos. We introduce GRASP, a large-scale social reasoning dataset that connects high-level social QA with fine-grained gaze and deictic gesture events. GRASP contains 290K question--answer pairs over 46K videos totaling 749 hours, organized by a 16-category taxonomy spanning gaze, gesture, and joint gaze--gesture reasoning, together with GRASP-Bench for evaluation. Unlike prior resources that focus on either isolated cues or high-level social QA, GRASP builds questions from identity-consistent gaze trajectories, deictic gestures, and their joint compositions into social events. Moreover, we propose Social Grounding Reward (SGR), a learning signal that uses these social events to encourage models to reason about the participants involved in each interaction. Experiments show that SGR improves performance on GRASP-Bench while maintaining zero-shot performance on related social video QA benchmarks. |
cite
@misc{kim2026grasp,
title={GRASP: Learning to Ground Social Reasoning in Multi-Person Non-Verbal Interactions},
author={Junho Kim and Xu Cao and Houze Yang and Bikram Boote and Ana Jojic and Fiona Ryan and Bolin Lai and Sangmin Lee and James M. Rehg},
year={2026},
eprint={2605.15764},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2605.15764}
}