用生成式AI提升工业场景人体动作模拟的逼真度
Generative AI-Driven High-Fidelity Human Motion Simulation
- 结合文本到动作模型与语言理解，生成更符合任务描述的动作
- 在8个任务中7项表现优于人工描述，显著降低关节误差和时间错位
- 适合工业安全评估、人机交互设计等需要高保真动作模拟的场景
人体动作模拟（HMS）可低成本评估工业任务中工人的行为、安全与效率。现有方法常因动作保真度低而受限。本文提出生成式AI驱动的人体动作模拟框架（G-AI-HMS），融合文本到文本与文本到动作模型，提升物理任务模拟质量。该框架解决两大关键问题：（1）利用与MotionGPT训练词汇对齐的大语言模型，将任务描述转化为运动感知的语义表达；（2）通过计算机视觉技术验证生成动作的真实性——实时视频分析提取关节点坐标，使用运动相似性指标对比真实人类动作与AI生成序列。在8个任务的案例研究中，AI增强动作在空间精度上优于人工描述6次，姿态归一化后匹配度4次更优，整体时间相似性7次更佳。统计分析表明，AI增强提示显著降低了关节误差与时间错位（p < 0.0001），同时保持相近的姿态准确性。
原文摘要 · Abstract (English)
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.
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