Investigating the impact of fatigue on human physiological function and motor behavior is crucial for developing biomechanics and medical applications aimed at mitigating fatigue, reducing injury risk, and creating sophisticated ergonomic designs, as well as for producing physically-plausible 3D animation sequences. While the former has a prominent position in state-of-the-art literature, fatigue-driven motion generation is still an underexplored area. In this study, we present FatigueFusion, a deep-learning architecture for the fusion of fatigue features within a latent representation space, enabling the creation of a variation of novel fatigued movements, intermediate fatigued states, and progressively fatigued motions. Unlike existing approaches that focus on imitating the effects of fatigue accumulation in motion patterns, our framework incorporates algorithmic and data-driven modules to impose subject-specific temporal and spatial fatigue features on nonfatigued motions, while leveraging PINN-based techniques to simulate fatigue intensity. Since all motion modulation tasks are taking place in latent space, FatigueFusion offers an end-to-end architecture that operates directly on non-fatigued joint angle sequences and control parameters, allowing seamless integration into any motion synthesis pipeline, without relying on fatigue input data. Overall, our framework can be employed for various fatigue-driven synthesis tasks, such as fatigue profile transfer and fusion, while it also provides a solution for accurate rendering of the human fatigue state in both animation and simulation pipelines.

翻译：研究疲劳对人体生理功能及运动行为的影响，对于发展旨在缓解疲劳、降低损伤风险及构建精密人机工程设计的生物力学与医学应用，以及生成具有物理合理性的三维动画序列至关重要。尽管前者在现有文献中占据重要地位，但疲劳驱动的运动生成仍是一个探索不足的领域。本研究提出FatigueFusion——一种在潜表征空间内融合疲劳特征的深度学习架构，能够生成多样化的新型疲劳运动、中间疲劳状态及渐进疲劳运动。现有方法致力于模仿疲劳积累对运动模式的影响，而我们的框架整合了算法与数据驱动模块，在利用基于PINN的技术模拟疲劳强度的同时，将受试者特有的时域与空域疲劳特征施加于非疲劳运动。由于所有运动调制任务均在潜空间中完成，FatigueFusion提供了一种可直接作用于非疲劳关节角度序列与控制参数的端到端架构，无需依赖疲劳输入数据即可无缝集成至任意运动合成流水线。总体而言，该框架可应用于疲劳特征迁移与融合等多种疲劳驱动合成任务，同时为动画与仿真流水线中人体疲劳状态的精确渲染提供了解决方案。