  Anytime 3D human pose forecasting is crucial to synchronous real-world
human-machine interaction, where the term ``anytime" corresponds to predicting
human pose at any real-valued time step. However, to the best of our knowledge,
all the existing methods in human pose forecasting perform predictions at
preset, discrete time intervals. Therefore, we introduce AnyPose, a lightweight
continuous-time neural architecture that models human behavior dynamics with
neural ordinary differential equations. We validate our framework on the
Human3.6M, AMASS, and 3DPW dataset and conduct a series of comprehensive
analyses towards comparison with existing methods and the intersection of human
pose and neural ordinary differential equations. Our results demonstrate that
AnyPose exhibits high-performance accuracy in predicting future poses and takes
significantly lower computational time than traditional methods in solving
anytime prediction tasks.
