VoxelKeypointFusion: Generalizable Multi-View Multi-Person Pose Estimation

Daniel Bermuth, Alexander Poeppel, Wolfgang Reif

Abstract:

In the rapidly evolving field of computer vision, the task
of accurately estimating the poses of multiple individuals
from various viewpoints presents a formidable challenge,
especially if the estimations should be reliable as well. This
work presents an extensive evaluation of the generalization
capabilities of multi-view multi-person pose estimators to
unseen datasets and presents a new algorithm with strong
performance in this task. It also studies the improvements
by additionally using depth information. Since the new approach can not only generalize well to unseen datasets, but
also to different keypoints, the first multi-view multi-person
whole-body estimator is presented. To support further research on those topics, all of the work is publicly accessible.