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

2026-09-12

AI Summary

本文提出GEAR模型，通过动态激活个体运动和社会共鸣偏置项来改进社会轨迹预测，解决现有方法中社会信息编码后如何有效作用于未来轨迹生成的问题。

Abstract

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 context may require different activation strengths across future horizons and scene densities: social cues should be strengthened when interaction evidence is strong, but suppressed when they are weak or noisy. To address this issue, we propose GEAR, a generation-aware bias activation model for human trajectory prediction. Built upon a bias-decomposed trajectory generation formulation, GEAR dynamically activates the individual-motion and social-resonance bias terms at each future step before final trajectory composition. This allows the model to explicitly control when and how strongly individual and social bias components participate in generation. Experiments on ETH-UCY, SDD, and NBA show that GEAR consistently improves the resonance-based baseline and achieves competitive state-of-the-art performance. Further analyses of activation patterns and density-grouped errors validate the importance of calibrating encoded social context during trajectory generation. Our code is available at https://github.com/11isnotavailable/GEAR.git.

Problem

• human trajectory prediction
• social interaction
• dynamic activation
• context encoding
• trajectory generation

Innovation

• Dynamic Activation
• Generation-aware Bias
• Social Resonance
• Trajectory Prediction