Multimodal Quantitative Measures for Multiparty Behaviour Evaluation

• URL: http://arxiv.org/abs/2508.10916v1
• Date: Fri, 01 Aug 2025 13:46:12 GMT
• Title: Multimodal Quantitative Measures for Multiparty Behaviour Evaluation
• Authors: Ojas Shirekar, Wim Pouw, Chenxu Hao, Vrushank Phadnis, Thabo Beeler, Chirag Raman,
• Abstract summary: We introduce a unified, intervention-driven framework for objective assessment of multiparty social behaviour in skeletal motion data.<n>We validate metric sensitivity through three theory-driven perturbations.<n>Mixed-effects analyses reveal predictable, joint-independent shifts.
• Score: 6.709251546882382
• License: http://creativecommons.org/licenses/by/4.0/
• 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}.

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