Multi-Person 3D Pose Estimation With Occlusion Reasoning

The performance of existing methods for multi-person 3D pose estimation in crowded scenes is still limited, due to the challenge of heavy overlapping among persons. Attempt to address this issue, we propose a progressive inference scheme, i.e., Articulation-aware Knowledge Exploration (AKE), to impr...

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Bibliographic Details
Published in:IEEE transactions on multimedia 2024, Vol.26, p.878-889
Main Authors: Chen, Xipeng, Zhang, Junzheng, Wang, Keze, Wei, Pengxu, Lin, Liang
Format: Article
Language:English
Subjects:
3D human pose estimation
articulated structure
Heating systems
Kinematics
multi-person
occlusion reasoning
Optimization
Pose estimation
Shape
Solid modeling
Three-dimensional displays
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Summary:The performance of existing methods for multi-person 3D pose estimation in crowded scenes is still limited, due to the challenge of heavy overlapping among persons. Attempt to address this issue, we propose a progressive inference scheme, i.e., Articulation-aware Knowledge Exploration (AKE), to improve the multi-person 3D pose models on those samples with complex occlusions at the inference stage. We argue it is beneficial to explore the underlying articulated information/ knowledge of the human body, which helps to further correct the predicted poses in those samples. To exploit such information, we propose an iterative scheme to achieve a self-improving loop for keypoint association. Specifically, we introduce a kinematic validation module for locating unreasonable articulations and an occluded-keypoint discovering module for discovering occluded articulations. Extensive experiments on two challenging benchmarks under both weakly-supervised and fully-supervised settings demonstrate the superiority and generalization ability of our proposed method for crowded scenes.
ISSN:1520-9210
1941-0077
DOI:10.1109/TMM.2023.3272736