发表机构
TU Delft; University of Oxford（代尔夫特理工大学; 牛津大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对现有社交运动模型忽略交互状态的问题，提出双层潜在变量模型BRAID，通过群体级和个体级潜在状态联合建模多人交互，实现跨数据集元迁移，并在多种任务上验证其生成质量与可解释性。
AI 中文摘要
人类社交行为并非独立运动的集合，而是一个联合组织的过程，其中群体动态和个体变化不断相互塑造。然而，现有的社交运动模型往往优先考虑合理的轨迹，而将交互状态隐式化，限制了其跨群体、跨任务和部分观测场景的迁移能力。为解决这一差距，我们引入了用于智能体交互动力学的双层表示（BRAID），这是一种用于生成式多人交互的分层序列潜在变量模型。BRAID明确地将社交运动生成表述为一个元迁移学习问题：跨数据集学习共享的交互先验，并通过观测到的人和关节的任意上下文集进行适应。该模型通过群体级潜在状态来表示每个场景，该状态捕获共享的交互动力学，以及个体级潜在状态，该状态在演化的群体背景下捕获个体行为。这种建模选择使得在完全、稀疏或部分观测下能够生成连贯的运动，同时暴露紧凑的社交状态向量，可作为下游具身智能体系统的接口。我们在统一的基于SMPL的表征下，使用不仅评估重建精度还评估真实性、多样性、时间对齐和人际协调的指标，对BRAID进行了社交预测、跟踪和填充以及响应生成的评估。我们进一步分析了分层潜在空间，表明它捕获了可分离的群体和个体级结构。
英文摘要
Human social behaviour is not a collection of independent motions, but a jointly organised process in which group dynamics and individual variation continuously shape one another. Yet existing social motion models often prioritise plausible trajectories while leaving interaction state implicit, limiting their ability to transfer across groups, tasks, and partial-observation regimes. To address this gap, we introduce Bilevel Representations for Agent Interaction Dynamics (BRAID), a hierarchical sequential latent-variable model for generative multi-person interaction. BRAID explicitly formulates social motion generation as a meta-transfer learning problem: shared interaction priors are learned across datasets and adapted through arbitrary context sets of observed people and joints. The model represents each scene through a group-level latent state that captures shared interaction dynamics and person-level latent states that capture individual behaviour conditioned on the evolving group context. This modelling choice enables coherent generation under full, sparse, or partial observations while exposing compact social-state vectors that can serve as an interface for downstream embodied-agent systems. We evaluate BRAID under a unified SMPL-based representation on social forecasting, tracking and in-filling, and response generation, using metrics that assess not only reconstruction accuracy but also realism, diversity, temporal alignment, and interpersonal coordination. We further analyse the hierarchical latent space, showing that it captures separable group- and individual-level structure.
