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Related Experiment Video

Updated: Jun 2, 2026

Virtual Agent for Real-Time Motivational Interviewing by Integrating Adaptive Nonverbal Behavior and Language Models
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Published on: December 23, 2025

SocialGen: Modeling Multi-Human Social Interaction with Language Models.

Heng Yu1, Juze Zhang1, Changan Chen1

  • 1Stanford University.

Proceedings. International Conference on 3D Vision
|June 1, 2026
PubMed
Summary
This summary is machine-generated.

SocialGen is the first unified motion-language model for multi-human interactions. It uses a novel representation and the SocialX dataset to achieve state-of-the-art results in social behavior modeling.

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Human-Computer Interaction

Background:

  • Human interactions are complex and fundamental to many applications.
  • Existing models often struggle with interactions involving more than two people.
  • Bridging the gap between human motion and language understanding is crucial for social behavior modeling.

Purpose of the Study:

  • Introduce SocialGen, a unified motion-language model for multi-human interactions.
  • Develop a novel social motion representation for arbitrary numbers of individuals.
  • Establish a comprehensive benchmark and dataset for multi-human interaction tasks.

Main Methods:

  • Proposed a novel social motion representation supporting tokenization of multi-person motions.
  • Aligned motion representations with the language space to leverage linguistic knowledge.
  • Curated the SocialX dataset, a comprehensive multi-human interaction dataset with textual annotations.

Main Results:

  • Achieved state-of-the-art performance on motion-language tasks involving social interactions.
  • Demonstrated the effectiveness of the unified motion-language approach for multi-human modeling.
  • Established a new benchmark for evaluating multi-human interaction models.

Conclusions:

  • SocialGen advances the field of social behavior modeling by enabling the understanding of interactions among multiple individuals.
  • The proposed motion-language alignment and SocialX dataset provide a strong foundation for future research.
  • This work sets a new standard for multi-human interaction modeling and its applications.