SocialGen: Modeling Multi-Human Social Interaction with Language Models
Heng Yu, Juze Zhang, Chuanyue Chen, Tiange Xiang, Yusu Fang, Juan Carlos Niebles, Ehsan Adeli · 2026
Human interactions in everyday life are inherently social, involving engagements with diverse individuals across various contexts. Modeling these social interactions is fundamental to a wide range of real-world applications. In this paper, we introduce SocialGen, the first unified motionlanguage model capable of modeling interaction behaviors among varying numbers of individuals, to address this crucial yet challenging problem. Unlike prior methods that are limited to two-person interactions, we propose a novel social motion representation that supports tokenizing the motions of an arbitrary number of individuals and aligning them with the language space. This alignment enables the model to leverage rich, pretrained linguistic knowledge to better understand and reason about human social behaviors. To tackle the challenges of data scarcity, we curate a comprehensive multi-human interaction dataset, SocialX, enriched with textual annotations. Leveraging this dataset, we establish the first comprehensive benchmark for multihuman interaction tasks. Our method achieves state-of-theart performance across motion-language tasks, setting a new standard for multi-human interaction modeling. Our dataset and source code will be made publicly available.