SimpleDepthPose: Fast and Reliable Human Pose Estimation with RGBD-Images

Fuente: arXiv
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Main Authors: Bermuth, Daniel, Poeppel, Alexander, Reif, Wolfgang
Format: Preprint
Published: 2025
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author Bermuth, Daniel
Poeppel, Alexander
Reif, Wolfgang
author_facet Bermuth, Daniel
Poeppel, Alexander
Reif, Wolfgang
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2501_18478
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle SimpleDepthPose: Fast and Reliable Human Pose Estimation with RGBD-Images
Bermuth, Daniel
Poeppel, Alexander
Reif, Wolfgang
Computer Vision and Pattern Recognition
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.
title SimpleDepthPose: Fast and Reliable Human Pose Estimation with RGBD-Images
topic Computer Vision and Pattern Recognition
url https://arxiv.org/abs/2501.18478