GEAR: From Dynamic Encoding to Dynamic Activation in Social Trajectory Prediction
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: s...
| Authors | Jiaheng Chen, Jiaxing Li, Leixia Wang et al. |
|---|---|
| Primary category | cs.CV |
| Published | 12 September 2026 |
| Last updated at source | 12 September 2026 |
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