VoxelKeypointFusion: Generalizable Multi-View Multi-Person Pose Estimation
This work addresses the problem of reliable pose estimation across diverse viewpoints for computer vision applications, with incremental improvements in generalization.
The paper tackles the challenge of generalizing multi-view multi-person pose estimation to unseen datasets and different keypoints, presenting a new algorithm that achieves strong performance and introduces the first multi-view multi-person whole-body estimator.
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.