Human trajectory prediction is typically posed as a zero-shot generalization
problem: a predictor is learnt on a dataset of human motion in training scenes,
and then deployed on unseen test scenes. While this paradigm has yielded
tremendous progress, it fundamentally assumes that trends in human behavior
within the deployment scene are constant over time. As such, current prediction
models are unable to adapt to scene-specific transient human behaviors, such as
crowds temporarily gathering to see buskers, pedestrians hurrying through the
rain and avoiding puddles, or a protest breaking out. We formalize the problem
of scene-specific adaptive trajectory prediction and propose a new adaptation
approach inspired by prompt tuning called latent corridors. By augmenting the
input of any pre-trained human trajectory predictor with learnable image
prompts, the predictor can improve in the deployment scene by inferring trends
from extremely small amounts of new data (e.g., 2 humans observed for 30
seconds). 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 Wildtrack
real pedestrian data. Qualitatively, we observe that latent corridors imbue
predictors with an awareness of scene geometry and scene-specific human
behaviors that non-adaptive predictors struggle to capture. The project website
can be found at this https URL

使用可学习的图像提示将预训练的人体轨迹预测器的输入增强，从而使预测器能够从极少量的新数据中推断出部署场景的趋势，从而解决特定场景下的人体轨迹预测问题。