{"ID":2887982,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.10916","arxiv_id":"2508.10916","title":"Multimodal Quantitative Measures for Multiparty Behaviour Evaluation","abstract":"Digital humans are emerging as autonomous agents in multiparty interactions, yet existing evaluation metrics largely ignore contextual coordination dynamics. We introduce a unified, intervention-driven framework for objective assessment of multiparty social behaviour in skeletal motion data, spanning three complementary dimensions: (1) synchrony via Cross-Recurrence Quantification Analysis, (2) temporal alignment via Multiscale Empirical Mode Decompositionbased Beat Consistency, and (3) structural similarity via Soft Dynamic Time Warping. We validate metric sensitivity through three theory-driven perturbations -- gesture kinematic dampening, uniform speech-gesture delays, and prosodic pitch-variance reduction-applied to $\\approx 145$ 30-second thin slices of group interactions from the DnD dataset. Mixed-effects analyses reveal predictable, joint-independent shifts: dampening increases CRQA determinism and reduces beat consistency, delays weaken cross-participant coupling, and pitch flattening elevates F0 Soft-DTW costs. A complementary perception study ($N=27$) compares judgments of full-video and skeleton-only renderings to quantify representation effects. Our three measures deliver orthogonal insights into spatial structure, timing alignment, and behavioural variability. Thereby forming a robust toolkit for evaluating and refining socially intelligent agents. Code available on \\href{https://github.com/tapri-lab/gig-interveners}{GitHub}.","short_abstract":"Digital humans are emerging as autonomous agents in multiparty interactions, yet existing evaluation metrics largely ignore contextual coordination dynamics. We introduce a unified, intervention-driven framework for objective assessment of multiparty social behaviour in skeletal motion data, spanning three complementar...","url_abs":"https://arxiv.org/abs/2508.10916","url_pdf":"https://arxiv.org/pdf/2508.10916v1","authors":"[\"Ojas Shirekar\",\"Wim Pouw\",\"Chenxu Hao\",\"Vrushank Phadnis\",\"Thabo Beeler\",\"Chirag Raman\"]","published":"2025-08-01T13:46:12Z","proceeding":"cs.HC","tasks":"[\"cs.HC\",\"cs.AI\",\"cs.CY\",\"cs.MA\"]","methods":"[]","has_code":false,"code_links":[{"ID":611495,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2887982,"paper_url":"https://arxiv.org/abs/2508.10916","paper_title":"Multimodal Quantitative Measures for Multiparty Behaviour Evaluation","repo_url":"https://github.com/tapri-lab/gig-interveners","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}