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ChatMotion: A Multimodal Multi-Agent for Human Motion Analysis

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arxiv 2502.18180 v2 pith:NXJEKJX4 submitted 2025-02-25 cs.AI cs.MA

classification cs.AIcs.MA
keywords motionhumanchatmotionmultimodaladaptabilityanalysismodelsmodules
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Advancements in Multimodal Large Language Models (MLLMs) have improved human motion understanding. However, these models remain constrained by their "instruct-only" nature, lacking interactivity and adaptability for diverse analytical perspectives. To address these challenges, we introduce ChatMotion, a multimodal multi-agent framework for human motion analysis. ChatMotion dynamically interprets user intent, decomposes complex tasks into meta-tasks, and activates specialized function modules for motion comprehension. It integrates multiple specialized modules, such as the MotionCore, to analyze human motion from various perspectives. Extensive experiments demonstrate ChatMotion's precision, adaptability, and user engagement for human motion understanding.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Human Motion Instruction Tuning

    cs.AI 2024-11 conditional novelty 5.0 of 10

    A language model trained on continuous motion features, video, and text with text-guided frame selection improves scores on motion understanding benchmarks.

  2. Evolutionary Physics-Informed Temporal Fusion for Lane-Change Intention Prediction

    cs.LG 2025-12 reject novelty 4.0 of 10

    A hybrid Bi-LSTM plus LightGBM model with physics-inspired handcrafted features is claimed to improve lane-change intention prediction on highD and exiD, but the reported gains are compromised by test-set leakage and ...

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