Generative AI-Driven High-Fidelity Human Motion Simulation
This work addresses the need for cost-effective and high-fidelity simulation of worker behavior in industrial settings, representing an incremental improvement over existing methods.
The study tackled the problem of low motion fidelity in human motion simulation for industrial tasks by introducing Generative-AI-Enabled HMS, which integrated text-to-text and text-to-motion models to enhance simulation quality, resulting in AI-enhanced motions showing lower error than human-created descriptions in most scenarios, with significant reductions in joint error and temporal misalignment.
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.