Generative AI-Driven High-Fidelity Human Motion Simulation

TL;DR

This paper presents G-AI-HMS, a novel generative AI framework that significantly improves the fidelity of human motion simulations for industrial tasks by translating task descriptions into realistic motions and validating them against real human movements.

Contribution

Introduces G-AI-HMS, integrating large language models and motion validation techniques to enhance the realism and accuracy of human motion simulations in industrial contexts.

Findings

AI-enhanced motions outperformed human descriptions in spatial accuracy.

Statistical analysis confirmed significant reduction in joint error and temporal misalignment.

Method achieved high similarity to real human movements across multiple metrics.

Abstract

Human motion simulation (HMS) supports cost-effective evaluation of worker behavior, safety, and productivity in industrial tasks. However, existing methods often suffer from low motion fidelity. This study introduces Generative-AI-Enabled HMS (G-AI-HMS), which integrates text-to-text and text-to-motion models to enhance simulation quality for physical tasks. G-AI-HMS tackles two key challenges: (1) translating task descriptions into motion-aware language using Large Language Models aligned with MotionGPT's training vocabulary, and (2) validating AI-enhanced motions against real human movements using computer vision. Posture estimation algorithms are applied to real-time videos to extract joint landmarks, and motion similarity metrics are used to compare them with AI-enhanced sequences. In a case study involving eight tasks, the AI-enhanced motions showed lower error than human created descriptions in most scenarios, performing better in six tasks based on spatial accuracy, four tasks based on alignment after pose normalization, and seven tasks based on overall temporal similarity. Statistical analysis showed that AI-enhanced prompts significantly (p $<$ 0.0001) reduced joint error and temporal misalignment while retaining comparable posture accuracy.