Diverse Yet Consistent: Context-Guided Diffusion with Energy-Based Joint Refinement for Multi-Agent Motion Prediction

2026-05-22 · Source: cs.CV updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Expert, extended

Summary

The CODA framework introduces a novel approach to multi-agent motion prediction, leveraging context-guided diffusion with energy-based joint refinement to generate predictions that are both diverse and jointly consistent. This deep generative model addresses challenges in capturing multimodal distributions and ensuring interaction consistency among agents. CODA integrates Dynamic Context as Guidance Condition (DCGC) and an Adaptive Condition Integration Module (ACIM) to enhance diversity, alongside a Joint Distribution Refinement (JDR) module using an energy-based formulation to enforce consistency. Extensive experiments on four benchmark datasets—ETH/UCY, SDD, NBA, and JRDB—demonstrate CODA's superior performance. It achieved the lowest average error (0.17/0.28) on ETH/UCY, a 15.0%/12.5% improvement over MoFlow (0.20/0.32), and established state-of-the-art marginal performance on SDD (ADE 6.87, FDE 10.86) and NBA (ADE/FDE at 4.0s: 0.69/0.87), while maintaining competitive joint metrics.

Key takeaway

For Machine Learning Engineers developing multi-agent systems, especially in autonomous driving or robotics, CODA offers a robust method to improve trajectory forecasting. Its context-guided diffusion and energy-based refinement approach allows you to generate predictions that are both diverse and jointly consistent, which is crucial for safety and reliability. You should consider adopting this framework to balance individual agent plausibility with overall interaction coherence in your multi-agent motion prediction models.

Key insights

Context-guided diffusion with energy-based refinement improves diverse yet consistent multi-agent motion prediction.

Principles

• Leverage historical context for diverse predictions.
• Energy-based models refine joint consistency.
• Marginal and joint metrics require distinct optimization.

Method

CODA uses DCGC for dynamic features, ACIM for context integration via cross-attention, and JDR with an energy-based model to refine joint trajectory distributions.

In practice

• Integrate contextual information into diffusion models.
• Apply energy-based models for multi-agent consistency.
• Evaluate with both marginal (ADE/FDE) and joint (JADE/JFDE) metrics.

Topics

• Multi-Agent Motion Prediction
• Diffusion Models
• Energy-Based Models
• Trajectory Forecasting
• Contextual Guidance
• Deep Generative Models

Best for: Research Scientist, Computer Vision Engineer, AI Scientist, Machine Learning Engineer, Robotics Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CV updates on arXiv.org.