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

• URL: http://arxiv.org/abs/2309.04840v1
• Date: Sat, 9 Sep 2023 16:59:57 GMT
• Title: AnyPose: Anytime 3D Human Pose Forecasting via Neural Ordinary Differential Equations
• Authors: Zixing Wang, Ahmed H. Qureshi
• Abstract summary: AnyPose is a lightweight continuous-time neural architecture that models human behavior dynamics with 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.
• Score: 2.7195102129095003
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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