PoseGraphNet++: Enriching 3D Human Pose with Orientation Estimation
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866913345526824960 |
|---|---|
| author | Banik, Soubarna Avagyan, Edvard Auddy, Sayantan Gracia, Alejandro Mendoza Knoll, Alois |
| author_facet | Banik, Soubarna Avagyan, Edvard Auddy, Sayantan Gracia, Alejandro Mendoza Knoll, Alois |
| contents | Existing skeleton-based 3D human pose estimation methods only predict joint positions. Although the yaw and pitch of bone rotations can be derived from joint positions, the roll around the bone axis remains unresolved. We present PoseGraphNet++ (PGN++), a novel 2D-to-3D lifting Graph Convolution Network that predicts the complete human pose in 3D including joint positions and bone orientations. We employ both node and edge convolutions to utilize the joint and bone features. Our model is evaluated on multiple datasets using both position and rotation metrics. PGN++ performs on par with the state-of-the-art (SoA) on the Human3.6M benchmark. In generalization experiments, it achieves the best results in position and matches the SoA in orientation, showcasing a more balanced performance than the current SoA. PGN++ exploits the mutual relationship of joints and bones resulting in significantly \SB{improved} position predictions, as shown by our ablation results. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2308_11440 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | PoseGraphNet++: Enriching 3D Human Pose with Orientation Estimation Banik, Soubarna Avagyan, Edvard Auddy, Sayantan Gracia, Alejandro Mendoza Knoll, Alois Computer Vision and Pattern Recognition Existing skeleton-based 3D human pose estimation methods only predict joint positions. Although the yaw and pitch of bone rotations can be derived from joint positions, the roll around the bone axis remains unresolved. We present PoseGraphNet++ (PGN++), a novel 2D-to-3D lifting Graph Convolution Network that predicts the complete human pose in 3D including joint positions and bone orientations. We employ both node and edge convolutions to utilize the joint and bone features. Our model is evaluated on multiple datasets using both position and rotation metrics. PGN++ performs on par with the state-of-the-art (SoA) on the Human3.6M benchmark. In generalization experiments, it achieves the best results in position and matches the SoA in orientation, showcasing a more balanced performance than the current SoA. PGN++ exploits the mutual relationship of joints and bones resulting in significantly \SB{improved} position predictions, as shown by our ablation results. |
| title | PoseGraphNet++: Enriching 3D Human Pose with Orientation Estimation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2308.11440 |