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

• URL: http://arxiv.org/abs/2607.05733v1
• Date: Tue, 07 Jul 2026 01:37:38 GMT
• Title: ARMS: Anchor-Relational Motion Streaming for Seamless Solo-Social Motion Transitions
• Abstract summary: We propose an Anchor-Relational Motion Streaming framework that unifies solo motion and human-human interaction.<n>We show that ARMS improves transition smoothness and social coherence compared to interaction-centric baselines.
• Score: 30.111598584743774
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

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