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{this https URL}{GitHub}.
Abstract (translated)
数字人类正在作为多方互动中的自主代理出现，然而现有的评估指标大多忽略了情境协调动态。我们引入了一个统一的、以干预驱动为主的框架，用于基于骨骼运动数据客观地评估多方社交行为，涵盖三个互补维度：（1）通过交叉回归量化分析(CRQA)来衡量同步性；（2）通过多尺度经验模式分解(MEMD)基础节拍一致性来衡量时间对齐；以及（3）通过软动态时间规整(Soft-DTW)来衡量结构相似度。我们通过对从DnD数据集中提取的约145个30秒长度的小组互动片段进行三种理论驱动干扰——手势动力学减弱、统一的语言-动作延迟和音调变异性减少——验证了这些指标的敏感性。混合效应分析揭示了可预测且独立于关节变化的趋势：动力减弱增加了CRQA决定论并降低了节拍一致性，延迟削弱了参与者之间的耦合，而音调扁平化提高了F0 Soft-DTW成本。 一项补充感知研究（N=27）比较了全视频和骨骼动画渲染的评判结果，以量化表示效果。我们的三项度量分别提供了空间结构、时间对齐和行为变异性方面的独立见解。因此形成了一套评估并改进社交智能代理的稳健工具包。代码可在GitHub上获得。 原文链接：[此处应为一个有效的GitHub链接]
URL
https://arxiv.org/abs/2508.10916
