MPL: Lifting 3D Human Pose from Multi-view 2D Poses

• URL: http://arxiv.org/abs/2408.10805v1
• Date: Tue, 20 Aug 2024 12:55:14 GMT
• Title: MPL: Lifting 3D Human Pose from Multi-view 2D Poses
• Authors: Seyed Abolfazl Ghasemzadeh, Alexandre Alahi, Christophe De Vleeschouwer,
• Abstract summary: 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. Our experiments demonstrate decreases up to 45% in MPJPE errors compared to the 3D pose obtained by triangulating the 2D poses.
• Score: 75.26416079541723
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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 .

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