2024/10/24 by Daniel Bermuth, Bermuth, Daniel, Alexander Poeppel +3 · 1 citation
Computer Science · #Anomaly Detection Techniques and Applications #Artificial intelligence #Computer science #Computer vision #Economics #Estimation #Hand Gesture Recognition Systems #Human Pose and Action Recognition #Machine learning #Management #Pose
paper · pdf · doi:10.48550/arxiv.2410.18723
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2024/10/24 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.