MPL: Lifting 3D Human Pose from Multi-view 2D Poses
Fuente:
arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866910571125800960 |
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| author | Ghasemzadeh, Seyed Abolfazl Alahi, Alexandre De Vleeschouwer, Christophe |
| author_facet | Ghasemzadeh, Seyed Abolfazl Alahi, Alexandre De Vleeschouwer, Christophe |
| contents | Estimating 3D human poses from 2D images is challenging due to occlusions and projective acquisition. Learning-based approaches have been largely studied to address this challenge, both in single and multi-view setups. These solutions however fail to generalize to real-world cases due to the lack of (multi-view) 'in-the-wild' images paired with 3D poses for training. For this reason, we propose combining 2D pose estimation, for which large and rich training datasets exist, and 2D-to-3D pose lifting, using a transformer-based network that can be trained from synthetic 2D-3D pose pairs. Our experiments demonstrate decreases up to 45% in MPJPE errors compared to the 3D pose obtained by triangulating the 2D poses. The framework's source code is available at https://github.com/aghasemzadeh/OpenMPL . |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_10805 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | MPL: Lifting 3D Human Pose from Multi-view 2D Poses Ghasemzadeh, Seyed Abolfazl Alahi, Alexandre De Vleeschouwer, Christophe Computer Vision and Pattern Recognition Estimating 3D human poses from 2D images is challenging due to occlusions and projective acquisition. Learning-based approaches have been largely studied to address this challenge, both in single and multi-view setups. These solutions however fail to generalize to real-world cases due to the lack of (multi-view) 'in-the-wild' images paired with 3D poses for training. For this reason, we propose combining 2D pose estimation, for which large and rich training datasets exist, and 2D-to-3D pose lifting, using a transformer-based network that can be trained from synthetic 2D-3D pose pairs. Our experiments demonstrate decreases up to 45% in MPJPE errors compared to the 3D pose obtained by triangulating the 2D poses. The framework's source code is available at https://github.com/aghasemzadeh/OpenMPL . |
| title | MPL: Lifting 3D Human Pose from Multi-view 2D Poses |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2408.10805 |