Fatigue-PINN applies physics-informed neural networks to simulate fatigue effects on human motion using a three-compartment muscle model for joint torque modulation in motion synthesis.
A unified framework for multimodal, multi-part human motion synthesis
2 Pith papers cite this work. Polarity classification is still indexing.
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Transformer generative model produces emotional body motions from Japanese motion-capture data, achieving 22.8% machine and 24.9% human recognition accuracy, with demonstrated utility for augmenting recognition models, extracting patterns, and synthesizing transitions.
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Fatigue-PINN: Physics-Informed Fatigue-Driven Motion Modulation and Synthesis
Fatigue-PINN applies physics-informed neural networks to simulate fatigue effects on human motion using a three-compartment muscle model for joint torque modulation in motion synthesis.
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Generative Learning as a Tool to Improve Perception of Emotional Body Motion Expressions
Transformer generative model produces emotional body motions from Japanese motion-capture data, achieving 22.8% machine and 24.9% human recognition accuracy, with demonstrated utility for augmenting recognition models, extracting patterns, and synthesizing transitions.