HAP: A Hand-Driven Active Perception Framework for Egocentric Head Motion Prediction

The paper introduces HAP, a Hand-Driven Active Perception framework that predicts future six‑degree‑of‑freedom head motion in egocentric settings by conditioning on observed hand motion and inferred target context. HAP constructs a Predictive Target‑Centric Amodal Occlusion Graph to model current and potential occlusions among candidate objects, fuses this with hand and head motion history, and blends the learned trajectory with a constant‑velocity prior. Experiments on a public dataset and a newly released Bottle RGB‑D dataset demonstrate that HAP outperforms baseline methods in head‑motion prediction, highlighting the importance of hand‑driven intention and dynamic occlusion reasoning.