ARMS: Anchor-Relational Motion Streaming for Seamless Solo-Social Motion Transitions

2026-07-06

AI Summary

This work addresses the challenge of generating long-term, socially coherent human motion that naturally transitions between solitary and interactive states. The authors propose a unified causal generative framework that seamlessly integrates individual motion and interpersonal interaction within a single motion stream, enabling incremental long-horizon generation conditioned solely on historical context. Key innovations include a dynamically asymmetric representation to decouple individual temporal evolution from interpersonal alignment, a mode-aware relational gating mechanism for smooth transitions between generation modes, and a diffusion model operating in a causal latent space augmented with partner-referenced relative translation representations. Experiments demonstrate that the method outperforms existing interaction-centric baselines in terms of smoothness during solitary-to-social transitions and social coherence, while achieving competitive performance on standard human-human interaction benchmarks.

Abstract

Generating temporally continuous and socially coherent human motion from text remains a fundamental challenge, particularly in realistic streams where people act alone, enter interactions, and later disengage. Most existing methods generate fixed-length motion clips under static agent configurations, which makes them brittle to solo-social transitions and unsuitable for incremental generation over long horizons. We propose ARMS, an Anchor-Relational Motion Streaming framework that unifies solo motion and human-human interaction within a single causal generative process. ARMS introduces a dynamics-asymmetric representation that decouples per-person temporal evolution from inter-person alignment via a partner-referenced relative-translation term, enabling seamless switching of social coupling without sacrificing long-horizon stability or spatial consistency between agents. On top of a causal latent space, a causal relational diffusion model progressively refines motion segment by segment using only past context, capturing both intra-person temporal dependencies and inter-person relations. Mode-aware relational gating activates or masks cross-agent connections, allowing the same model to support both solo and interaction generation. Experiments show that ARMS improves transition smoothness and social coherence compared to interaction-centric baselines, while also achieving competitive results on human-human interaction benchmarks.

Problem

• motion generation
• solo-social transition
• temporal continuity
• social coherence
• incremental generation

Innovation

• causal relational diffusion
• dynamics-asymmetric representation
• relative-translation term
• mode-aware relational gating
• motion streaming