SimpleDepthPose: Fast and Reliable Human Pose Estimation with RGBD-Images
This addresses the challenge of reliable human pose estimation in computer vision, though it appears incremental as it builds on existing RGBD-based methods.
The paper tackles multi-view, multi-person pose estimation by incorporating depth information, achieving fast runtime performance and good generalization to unseen datasets.
In the rapidly advancing domain of computer vision, accurately estimating the poses of multiple individuals from various viewpoints remains a significant challenge, especially when reliability is a key requirement. This paper introduces a novel algorithm that excels in multi-view, multi-person pose estimation by incorporating depth information. An extensive evaluation demonstrates that the proposed algorithm not only generalizes well to unseen datasets, and shows a fast runtime performance, but also is adaptable to different keypoints. To support further research, all of the work is publicly accessible.