Abstract
Estimating the 3D model of the human body is needed for many applications. However, this is a challenging problem since the human body inherently has a high complexity due to self-occlusions and articulation. We present a method to reconstruct the 3D human body model from a single RGB-D image. 2D joint points are firstly predicted by a CNN-based model called convolutional pose machine, and the 3D joint points are calculated using the depth image. Then, we propose to utilize both 2D and 3D joint points, which provide more information, to fit a parametric body model (SMPL). This is implemented through minimizing an objective function, which measures the difference of the joint points between the observed model and the parametric model. The pose and shape parameters of the body are obtained through optimization and the final 3D model is estimated. The experiments on synthetic data and real data demonstrate that our method can estimate the 3D human body model correctly.
Original language | English |
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Title of host publication | ICPRAM 2019 - Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods |
Editors | Ana Fred, Maria De Marsico, Gabriella Sanniti di Baja |
Publisher | SciTePress |
Pages | 574-581 |
Number of pages | 8 |
ISBN (Electronic) | 9789897583513 |
DOIs | |
Publication status | Published - 2019 |
Event | 8th International Conference on Pattern Recognition Applications and Methods, ICPRAM 2019 - Prague, Czech Republic Duration: 2019 Feb 19 → 2019 Feb 21 |
Conference
Conference | 8th International Conference on Pattern Recognition Applications and Methods, ICPRAM 2019 |
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Country/Territory | Czech Republic |
City | Prague |
Period | 2019/02/19 → 2019/02/21 |
Subject classification (UKÄ)
- Computer Vision and Robotics (Autonomous Systems)
Keywords
- 2D and 3D Pose
- Human Body Reconstruction
- Pose
- Shape Estimation
- SMPL Model