arXiv AI By Zhongju Wang, Beier Wang, Yatao Bian, Pichao Wang, Zhi Wang, Daoyi Dong, Hongdong Li, Huadong Mo, Zhenhong Sun

Social Structure Matters in 3D Human-Human Interaction Generation

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arXiv:2606. 24255v1 Announce Type: cross Abstract: Although text-to-motion generation has achieved strong progress in synthesizing realistic single-person motions from language, extending it to text-driven 3D human-human interaction (HHI) remains non-trivial, as HHI requires modeling the underlying \textbf{social structure} that governs phase progression, actor roles, and inter-actor coordination.

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arXiv AI
4d ago

Generative Interactions: Weaving Multiparty Human Motion with Bilevel Latent Dynamics

The paper introduces BRAID, a hierarchical latent-variable model that generates multi-person human motion by explicitly modeling both group-level interaction dynamics and individual behavior conditioned on evolving group context. It treats social motion generation as a meta-transfer learning problem, learning shared interaction priors across datasets and adapting them to arbitrary context sets of observed people and joints. BRAID supports coherent generation under full, sparse, or partial observations and produces compact social-state vectors useful for downstream embodied-agent systems, with evaluations on social forecasting, tracking, in-filling, and response generation.

By Ojas Shirekar, Yash Surange, Agustinas Ju\v{c}as, Chirag Raman