Papers › AnyPose: Anytime 3D Human Pose Forecasting via Neural Ordinary Differential Equations

AnyPose: Anytime 3D Human Pose Forecasting via Neural Ordinary Differential Equations

9 Sep 2023arXiv:2309.04840archive 2025-07-28

Zixing Wang, Ahmed H. Qureshi

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.

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