ReMoGen: Real-time Human Interaction-to-Reaction Generation via Modular Learning from Diverse Data

ArXi:2604.01082v1 Announce Type: new Human behaviors in real-world environments are inherently interactive, with an individual's motion shaped by surrounding agents and the scene. Such capabilities are essential for applications in virtual avatars, interactive animation, and human-robot collaboration. We target real-time human interaction-to-reaction generation, which generates the ego's future motion from dynamic multi-source cues, including others' actions, scene geometry, and optional high-level semantic inputs.