ARMS: Anchor-Relational Motion Streaming for Seamless Solo-Social Motion Transitions
This work addresses the challenge of generating long, temporally continuous human motion with realistic solo-social transitions, which is important for animation, robotics, and virtual reality applications.
ARMS introduces a causal generative framework for streaming human motion that seamlessly transitions between solo and interactive states, improving transition smoothness and social coherence over interaction-centric baselines while achieving competitive results on human-human interaction benchmarks.
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.