A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf
.
TRiPOD: Human Trajectory and Pose Dynamics Forecasting in the Wild
[article]
2021
arXiv
pre-print
Joint forecasting of human trajectory and pose dynamics is a fundamental building block of various applications ranging from robotics and autonomous driving to surveillance systems. Predicting body dynamics requires capturing subtle information embedded in the humans' interactions with each other and with the objects present in the scene. In this paper, we propose a novel TRajectory and POse Dynamics (nicknamed TRiPOD) method based on graph attentional networks to model the human-human and
arXiv:2104.04029v2
fatcat:pbsnyjefv5c2hhubsacxcmoo4u