Tue, Apr 16, 24, 3D human pose estimation is a computer vision technique that infers the three-dimensional positions of human body joints from 2D images or videos
Edit me
This is a draft, the content is not complete and of poor quality!
3d human pose estimation
video3DPoseEstimation
mediaPipe
mediaPipe2
msCOCO
awsCoachingApplication
awsAthleteTracking
3D human pose estimation in video with temporal convolutions and semi-supervised training
3D human pose estimation in video with temporal convolutions and semi-supervised training
3D human pose estimation in video with temporal convolutions and semi-supervised training This is the implementation of the approach described in the paper:
Dario Pavllo, Christoph Feichtenhofer, David Grangier, and Michael Auli. . In Conference on Computer Vision and Pattern Recognition (CVPR), 2019.
More demos are available at https://dariopavllo.github.io/VideoPose3D
Results on Human3.6M
Under Protocol 1 (mean per-joint position error) and Protocol 2 (mean-per-joint position error after rigid alignment).
| 2D Detections | BBoxes | Blocks | Receptive Field | Error (P1) | Error (P2) |
|---|---|---|---|---|---|
| CPN | Mask R-CNN | 4 | 243 frames | 46.8 mm | 36.5 mm |
| CPN | Ground truth | 4 | 243 frames | 47.1 mm | 36.8 mm |
| CPN | Ground truth | 3 | 81 frames | 47.7 mm | 37.2 mm |
| CPN | Ground truth | 2 | 27 frames | 48.8 mm | 38.0 mm |
| Mask R-CNN | Mask R-CNN | 4 | 243 frames | 51.6 mm | 40.3 mm |
| Ground truth | – | 4 | 243 frames | 37.2 mm | 27.2 mm |
AWS CDK
live streaming in realtime full frames
The following wiki, pages and posts are tagged with
| Title | Type | Excerpt |
|---|---|---|
| Exploring Jetson Nano in AIoT Applications | page | Jetson Nano serves as a potent platform for Edge AI applications, supporting popular frameworks like TensorFlow, PyTorch, and ONNX. Its compact form factor a... |