Residual Flow Matching with Dynamic Cross-Interaction for 3D Multi-Person Motion Prediction

• URL: http://arxiv.org/abs/2608.03379v1
• Date: Tue, 04 Aug 2026 09:28:27 GMT
• Title: Residual Flow Matching with Dynamic Cross-Interaction for 3D Multi-Person Motion Prediction
• Abstract summary: 3D multi-person motion prediction requires modeling both individual kinematics and inter-person interactions.<n> Flow Matching is effective for multi-hypothesis generation to improve prediction accuracy.<n>We propose a Prior-Guided Residual Flow Matching framework.
• Score: 10.207329247559242
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 3D multi-person motion prediction requires modeling both individual kinematics and inter-person interactions. While Flow Matching is effective for multi-hypothesis generation to improve prediction accuracy, directly predicting skeletal sequences from pure noise often compromises structural consistency and introduces unreliable cross-agent interactions during early noise-dominated integration steps. To address this, we propose a Prior-Guided Residual Flow Matching framework. First, a Deterministic Coarse Prior (DCP) establishes a kinematic anchor, formulating the generative process as a conditional flow over motion residuals to simplify the generative objective and preserve structural stability. Second, a Dynamic Cross-Interaction (DCI) mechanism temporally synchronizes inter-agent message-passing with the integration progress, ensuring the extraction of reliable social contexts and improving multi-person motion fidelity. Finally, a decoupled joint-motion architecture with bidirectional fusion effectively preserves fine-grained kinematic coherence. Extensive experiments demonstrate that our approach achieves state-of-the-art prediction accuracy across multiple datasets. Code is available at https://github.com/Wei-Wei-a/Residual-Flow-Matching-with-Dynamic-Cross-Interaction-for-3D-Multi-Pers on-Motion-Prediction.

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