{"ID":6023325,"CreatedAt":"2026-07-08T01:00:23.257252134Z","UpdatedAt":"2026-07-10T01:27:54.063497417Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.05733","arxiv_id":"2607.05733","title":"ARMS: Anchor-Relational Motion Streaming for Seamless Solo-Social Motion Transitions","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.","short_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 b...","url_abs":"https://arxiv.org/abs/2607.05733","url_pdf":"https://arxiv.org/pdf/2607.05733v1","authors":"[\"Huakun Liu\",\"Qing Yu\",\"Kent Fujiwara\",\"Hideaki Uchiyama\",\"Kiyoshi Kiyokawa\"]","published":"2026-07-07T01:37:38Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[\"Diffusion Model\"]","has_code":false}