Name
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. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.5220/0007383605740581 | ICPRAM: PROCEEDINGS OF THE 8TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION APPLICATIONS AND METHODS |
Keywords | Field | DocType |
Human Body Reconstruction, SMPL Model, 2D and 3D Pose, Pose and Shape Estimation | Human-body model,Parametric model,Pattern recognition,Computer science,Synthetic data,Parametric statistics,RGB color model,Artificial intelligence,Human body | Conference |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhongguo Li | 1 | 0 | 0.68 |
| anders heyden | 2 | 820 | 109.50 |
| Magnus Oskarsson | 3 | 196 | 22.85 |