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Human trajectory prediction is typically posed as a zero-shot generalizationproblem: a predictor is learnt on a dataset of human motion in training scenes,and then deployed on unseen test scenes. While this paradigm has yieldedtremendous progress, it fundamentally assumes that trends in human behaviorwithin the deployment scene are constant over time. As such, current predictionmodels are unable to adapt to scene-specific transient human behaviors, such ascrowds temporarily gathering to see buskers, pedestrians hurrying through therain and avoiding puddles, or a protest breaking out. We formalize the problemof scene-specific adaptive trajectory prediction and propose a new adaptationapproach inspired by prompt tuning called latent corridors. By augmenting theinput of any pre-trained human trajectory predictor with learnable imageprompts, the predictor can improve in the deployment scene by inferring trendsfrom extremely small amounts of new data (e.g., 2 humans observed for 30seconds). With less than 0.1% additional model parameters, we see up to 23.9%ADE improvement in MOTSynth simulated data and 16.4% ADE in MOT and Wildtrackreal pedestrian data. Qualitatively, we observe that latent corridors imbuepredictors with an awareness of scene geometry and scene-specific humanbehaviors that non-adaptive predictors struggle to capture. The project websitecan be found at https://neerja.me/atp_latent_corridors/.

Adaptive Human Trajectory Prediction via Latent Corridors