🤖 AI Summary
Existing methods for 3D multi-person motion prediction often generate skeletal sequences directly from noise, which frequently leads to structural inconsistencies and unreliable early-stage interactions. To address these limitations, this work proposes a prior-guided residual flow matching framework. The approach first leverages a deterministic coarse-grained motion prior to construct a residual conditional flow, thereby simplifying the generative objective. It further introduces a dynamic cross-interaction mechanism that enables temporally aligned multi-agent information synchronization during integration. Additionally, a decoupled joint-motion bidirectional fusion architecture is incorporated to preserve fine-grained motion consistency. Evaluated on multiple benchmark datasets, the proposed method achieves state-of-the-art performance, significantly improving prediction accuracy over existing approaches.
📝 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-Person-Motion-Prediction.