  Human pose forecasting is the task of predicting articulated human motion
given past human motion. There exists a number of popular benchmarks that
evaluate an array of different models performing human pose forecasting. These
benchmarks do not reflect that a human interacting system, such as a delivery
robot, observes and plans for the motion of the same individual over an
extended period of time. Every individual has unique and distinct movement
patterns. This is however not reflected in existing benchmarks that evaluate a
model's ability to predict an average human's motion rather than a particular
individual's. We reformulate the human motion forecasting problem and present a
model-agnostic personalization method. Motion forecasting personalization can
be performed efficiently online by utilizing a low-parametric time-series
analysis model that personalizes neural network pose predictions.
