Graph-Augmented Motion Encoding for Long-Term Human–Object Interaction Prediction in Egocentric Videos
Keywords:
egocentric video, human–object interaction, graph neural networks, motion encoding, long-term prediction, spatiotemporal reasoning, system architectureAbstract
Predicting human–object interactions from egocentric video over extended time horizons is a central challenge for assistive technologies, autonomous agents, and mixed-reality interfaces. This paper presents a graph-augmented motion encoding framework that addresses the limitations of existing temporal models by explicitly representing both spatial relationships and long-range motion dynamics within a structured graph topology. The proposed architecture integrates spatiotemporal feature extraction with a layered graph neural network that encodes interactions across objects, body parts, and scene elements over hundreds of frames. Unlike recurrent or transformer-based approaches that rely on sequential aggregation without explicit relational reasoning, the graph-augmented design captures compositional changes in motion patterns and allows the model to reason about partial occlusions and variable interaction durations. We analyze system-level trade-offs including graph sparsity versus computational overhead, temporal window selection, and generalization across diverse egocentric datasets. Deployment considerations for real-time inference on edge devices, sustainability of large-scale training, and fairness in interaction recognition across demographic and activity distributions are discussed. The framework demonstrates robust performance in long-horizon prediction benchmarks while maintaining interpretability through graph topology analysis. Policy implications for privacy-conscious egocentric data collection and annotation standards are also examined. This work contributes a structural perspective on motion encoding that bridges graph learning and temporal modeling for complex socio-technical systems.
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