Deep learning-based estimation of whole-body kinematics from multi-view images
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Abstract (arXiv)
It is necessary to analyze the whole-body kinematics (including joint locations and joint angles) to assess risks of fatal and musculoskeletal injuries in occupational tasks. Human pose estimation has gotten more attention in recent years as a method to minimize the errors in determining joint locations. However, the joint angles are not often estimated, nor is the quality of joint angle estimation assessed. In this paper, we presented an end-to-end approach on direct joint angle estimation from multi-view images. Our method leveraged the volumetric pose representation and mapped the rotation representation to a continuous space where each rotation was uniquely represented. We also presented a new kinematic dataset in the domain of residential roofing with a data processing pipeline to generate necessary annotations for the supervised training procedure on direct joint angle estimation. We achieved a mean angle error of 7.19^circ on the new Roofing dataset and 8.41^circ on the Human3.6M dataset, paving the way for employment of on-site kinematic analysis using multi-view images.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 19 of its 33 references that are in the map. First posted on arXiv 12 Jul 2023.
Builds on
19 of 33 references.
References · 19
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- 2019Learnable Triangulation of Human Posecited by 160
- 2015Human Pose Estimation with Iterative Error Feedbackcited by 121
- 2020Epipolar Transformerscited by 117
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