Learning When to Listen: Gated Affect Fusion for Human Motion Prediction

arXiv:2607.00296v1 Announce Type: cross Abstract: Human motion forecasting in unconstrained real-world videos remains challenging due to the ambiguity of future behaviors and the presence of noisy multimodal observations. While facial affect potentially provides complementary behavioral cues, its practical utility and mechanistic boundaries within motion forecasting frameworks remain poorly understood. In this work, we present a systematic study investigating the utility and temporal limitations of affect-conditioned forecasting in-the-wild. We establish a rigorous multimodal pipeline combinin

Source: arXiv cs.AI — read the full report at the original publisher.

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