MM-PTGNN uses cross-modal alignment to transfer personalized cybersickness traits from non-invasive sensors into a video encoder, enabling video-only inference at 88.4% reported accuracy.
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Towards Consumer-Grade Cybersickness Prediction: Multi-Model Alignment for Real-Time Vision-Only Inference
MM-PTGNN uses cross-modal alignment to transfer personalized cybersickness traits from non-invasive sensors into a video encoder, enabling video-only inference at 88.4% reported accuracy.