Xiufeng Liu
Capsule network with using shifted windows for 3D human pose estimation
Liu, Xiufeng; Zhao, Zhongqiu; Tian, Weidong; Liu, Binbin; He, Hongmei
Authors
Zhongqiu Zhao
Weidong Tian
Binbin Liu
Prof Mary He H.He5@salford.ac.uk
Professor in A.I. for Robotics
Abstract
3D human pose estimation (HPE) is a vital technology with diverse applications, enhancing precision in tracking, analyzing, and understanding human movements. However, 3D HPE from monocular videos presents significant challenges, primarily due to self-occlusion, which can partially hinder traditional neural networks’ ability to accurately predict these positions. To address this challenge, we propose a novel approach using a capsule network integrated with the shifted windows attention model (SwinCAP). It improves prediction accuracy by effectively capturing the spatial hierarchical relationships between different parts and objects. A Parallel Double Attention mechanism is applied in SwinCAP enhances both computational efficiency and modeling capacity, and a Multi-Attention Collaborative module is introduced to capture a diverse range of information, including both coarse and fine details. Extensive experiments demonstrate that our SwinCAP achieves better or comparable results to state-of-the-art models in the challenging task of viewpoint transfer on two commonly used datasets: Human3.6M and MPI-INF-3DHP.
Journal Article Type | Article |
---|---|
Acceptance Date | Feb 1, 2025 |
Online Publication Date | Feb 10, 2025 |
Publication Date | 2025-04 |
Deposit Date | Mar 21, 2025 |
Journal | Journal of Visual Communication and Image Representation |
Print ISSN | 1047-3203 |
Publisher | Elsevier |
Peer Reviewed | Peer Reviewed |
Volume | 108 |
Article Number | 104409 |
DOI | https://doi.org/10.1016/j.jvcir.2025.104409 |
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