FatigueFusion: Latent Space Fusion for Fatigue-Driven Motion Synthesis

2026-04-11

AI Summary

Existing methods struggle to generate realistic fatigued motions in the absence of authentic fatigue data. To address this limitation, this work proposes FatigueFusion, a novel framework that enables end-to-end synthesis of fatigued human motion without requiring real-world fatigue inputs. FatigueFusion operates in latent space by fusing personalized spatiotemporal fatigue characteristics into non-fatigued joint angle sequences. It integrates data-driven modeling with physics-informed neural networks (PINNs) to modulate motion dynamics and precisely control fatigue intensity. The approach supports fatigue state transfer, blending, and progressive simulation, allowing seamless integration into existing animation pipelines. This significantly enhances the diversity, realism, and controllability of synthesized fatigued motions.

Technology Category

• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Computer Vision: Motion & Tracking
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Web Mining and Content Analysis: Web data generation and simulation

Abstract

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.

Problem

• fatigue-driven motion synthesis
• latent space fusion
• human motor behavior
• physically-plausible animation
• fatigue modeling

Innovation

• Latent Space Fusion
• Fatigue-Driven Motion Synthesis
• Physics-Informed Neural Networks (PINNs)
• End-to-End Motion Modulation
• Subject-Specific Fatigue Modeling