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 human motion prediction for human-robot applications. 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.
@misc{nilavadi2025uptorunified3dhuman,
title = {UPTor: Unified 3D Human Pose Dynamics and Trajectory Prediction for Human-Robot Interaction},
author = {Nisarga Nilavadi and Andrey Rudenko and Timm Linder},
year = {2025},
eprint = {2505.14866},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2505.14866},
}