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GEAR: From Dynamic Encoding to Dynamic Activation in Social Trajectory Prediction

arXiv · AI, language, vision and robotics · article · Sep 12, 2026 · UTC

Human trajectory prediction requires modeling both individual motion patterns and social interactions among agents. Existing methods have made substantial progress by using attention mechanisms, graph structures, and temporal encoders to capture dynamic social context. However, most of them primarily focus on how social information is encoded, while paying less explicit attention to how the encoded social context should take effect during future trajectory generation. In this paper, we argue that dynamic social encoding does not necessarily imply dynamic social activation. The same interaction

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