mlsanityData to 7 Oct 2026

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

arXiv 2309.04840 ↗First posted 9 Sep 2023cs.CVcited by 4

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Abstract (arXiv)

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.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 25 of its 48 references that are in the map. First posted on arXiv 9 Sep 2023.

Builds on

25 of 48 references.

References · 25

  1. 2017Attention Is All You Needcited by 59,932
  2. 2018Neural Ordinary Differential Equationscited by 3,733
  3. 2014Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modelingcited by 2,341
  4. 2018An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modelingcited by 1,447
  5. 2019AMASS: Archive of Motion Capture as Surface Shapescited by 1,208
  6. 2017Convolutional Sequence to Sequence Learningcited by 550
  7. 2017On human motion prediction using recurrent neural networkscited by 336
  8. 2019Learning Trajectory Dependencies for Human Motion Predictioncited by 245
  9. 2020How to train your neural ODE: the world of Jacobian and kinetic regularizationcited by 228
  10. 2020Long-term Human Motion Prediction with Scene Contextcited by 191
  11. 2020History Repeats Itself: Human Motion Prediction via Motion Attentioncited by 164
  12. 2020A Spatio-temporal Transformer for 3D Human Motion Predictioncited by 133
13 more
  1. 2018Convolutional Sequence to Sequence Model for Human Dynamicscited by 115
  2. 2018Human Motion Prediction via Spatio-Temporal Inpaintingcited by 114
  3. 2020Stochastic Latent Residual Video Predictioncited by 106
  4. 2017Deep representation learning for human motion prediction and classificationcited by 97
  5. 2017The Pose Knows: Video Forecasting by Generating Pose Futurescited by 88
  6. 2018A Neural Temporal Model for Human Motion Predictioncited by 87
  7. 2021Space-Time-Separable Graph Convolutional Network for Pose Forecastingcited by 84
  8. 2020Learning to Encode Position for Transformer with Continuous Dynamical Modelcited by 61
  9. 2021Pose Transformers (POTR): Human Motion Prediction with Non-Autoregressive Transformerscited by 46
  10. 2019Imitation Learning for Human Pose Predictioncited by 44
  11. 2020Vid-ODE: Continuous-Time Video Generation with Neural Ordinary Differential Equationcited by 39
  12. 2018Bio-LSTM: A Biomechanically Inspired Recurrent Neural Network for 3D Pedestrian Pose and Gait Predictioncited by 13
  13. 2017Object Handover Prediction using Gaussian Processes clustered with Trajectory Classificationcited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 0; 2025: 2; 2026: 1.

4 papers.

  1. 2025VITA: Vision-to-Action Flow Matching Policycited by 23
  2. 2026Wow, wo, val! A Comprehensive Embodied World Model Evaluation Turing Testcited by 11
  3. 2023Recent Advances in Deterministic Human Motion Prediction: A Reviewcited by 1
  4. 2025UPTor: Unified 3D Human Pose Dynamics and Trajectory Prediction for Human-Robot Interactioncited by 1