ABSTRACT Simulating fatigue effects on human motion is essential both for conducting biomechanical analyses in order to develop overexertion and fatigue prevention and mitigation techniques, as well as to realistically render temporally evolved fatigued movements in animation and Extended Reality (XR) pipelines. In this work, we propose a framework to account for the dynamic injection of fatigue and fatigue‐induced characteristic features in non‐fatigued movements for data‐driven fatigue‐driven motion synthesis, an under‐explored scientific field in recent literature. To do so, we leverage an interplay between a siamese neural network and a Transformer‐based Fatigue Model to account for the encoding and sampling of fatigue features, while fatigue scalings are incorporated into motion via the state‐of‐the‐art Fatigue‐PINN . Our quantitative evaluation findings confirm the effectiveness and validity of our framework, while qualitative analysis shows that the fatigued motion sequences produced from our model are comparable with the observations of real‐world experimental studies investigating the impact of externally perceived fatigue in human motion. Moreover, we developed a demonstrator to showcase and assess the capability of our model to dynamically integrate fatigue scalings and fatigue features into motion sequences, while evaluating its ability to be seamlessly integrated into XR and motion synthesis environments.
Loi et al. (Fri,) studied this question.