UPTor: Unified 3D Human Pose Dynamics and Trajectory Prediction for Human-Robot Interaction
This addresses the need for unified motion prediction in robotics, though it is incremental as it builds on existing methods like graph attention networks and transformers.
The paper tackles the problem of simultaneously predicting full-body human pose dynamics and motion trajectories from short input sequences, achieving a compact, real-time, and accurate approach for human-aware navigation and human-robot interaction.
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/