mlsanityData to 14 Oct 2026

UPTor: Unified 3D Human Pose Dynamics and Trajectory Prediction for Human-Robot Interaction

arXiv 2505.14866 ↗First posted 20 May 2025cs.RO cs.CVcited by 1

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Abstract (arXiv)

We introduce a unified approach to forecast the dynamics of human keypoints along with the motion trajectory based on a short sequence of input poses. While many studies address either full-body pose prediction or motion trajectory prediction, only a few attempt to merge them. We propose a motion transformation technique to simultaneously predict full-body pose and trajectory key-points in a global coordinate frame. We utilize an off-the-shelf 3D human pose estimation module, a graph attention network to encode the skeleton structure, and a compact, non-autoregressive transformer suitable for real-time motion prediction for human-robot interaction and human-aware navigation. We introduce a human navigation dataset ``DARKO'' with specific focus on navigational activities that are relevant for human-aware mobile robot navigation. We perform extensive evaluation on Human3.6M, CMU-Mocap, and our DARKO dataset. In comparison to prior work, we show that our approach is compact, real-time, and accurate in predicting human navigation motion across all datasets. Result animations, our dataset, and code will be available at https://nisarganc.github.io/UPTor-page/

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 26 of its 44 references that are in the map. First posted on arXiv 20 May 2025.

Builds on

26 of 44 references.

References · 26

  1. 2017Attention Is All You Needcited by 60,361
  2. 2017Graph Attention Networkscited by 8,923
  3. 2018Self-Attention with Relative Position Representationscited by 1,120
  4. 2022Human Motion Diffusion Modelcited by 1,080
  5. 2018Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networkscited by 690
  6. 2017On human motion prediction using recurrent neural networkscited by 338
  7. 2019Human Motion Trajectory Prediction: A Surveycited by 232
  8. 2020Long-term Human Motion Prediction with Scene Contextcited by 192
  9. 2020DLow: Diversifying Latent Flows for Diverse Human Motion Predictioncited by 185
  10. 2023InterDiff: Generating 3D Human-Object Interactions with Physics-Informed Diffusioncited by 182
  11. 2020Transformer Networks for Trajectory Forecastingcited by 181
  12. 2019Multi-Agent Tensor Fusion for Contextual Trajectory Predictioncited by 131
14 more
  1. 2022Back to MLP: A Simple Baseline for Human Motion Predictioncited by 100
  2. 2023EqMotion: Equivariant Multi-agent Motion Prediction with Invariant Interaction Reasoningcited by 97
  3. 2022BeLFusion: Latent Diffusion for Behavior-Driven Human Motion Predictioncited by 82
  4. 2019Social Ways: Learning Multi-Modal Distributions of Pedestrian Trajectories with GANscited by 79
  5. 2022Progressively Generating Better Initial Guesses Towards Next Stages for High-Quality Human Motion Predictioncited by 66
  6. 2021Multi-Person 3D Motion Prediction with Multi-Range Transformerscited by 63
  7. 2021TRiPOD: Human Trajectory and Pose Dynamics Forecasting in the Wildcited by 52
  8. 2020Socially and Contextually Aware Human Motion and Pose Forecastingcited by 51
  9. 2021Pose Transformers (POTR): Human Motion Prediction with Non-Autoregressive Transformerscited by 46
  10. 2020MeTRAbs: Metric-Scale Truncation-Robust Heatmaps for Absolute 3D Human Pose Estimationcited by 35
  11. 2019Disentangling Human Dynamics for Pedestrian Locomotion Forecasting with Noisy Supervisioncited by 27
  12. 2021Simple Baseline for Single Human Motion Forecastingcited by 18
  13. 2023TransFusion: A Practical and Effective Transformer-based Diffusion Model for 3D Human Motion Predictioncited by 17
  14. 2023AnyPose: Anytime 3D Human Pose Forecasting via Neural Ordinary Differential Equationscited by 4

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026HumAIN: Human-Aware Implicit Social Robot Navigationcited by 0