Our great sponsors
-
deep-high-resolution-net.pytorch
The project is an official implementation of our CVPR2019 paper "Deep High-Resolution Representation Learning for Human Pose Estimation"
-
WorkOS
The modern identity platform for B2B SaaS. The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning.
The project is open and you can follow the developments on GitHub: https://github.com/mattiolato98/deadlift-visual-analyzer.
I am sure you have some great ideas in mind for your model design, but a quick tip (from having worked on a similar project) - it may be very challenging to simply take raw videos as input (especially with unconstrained camera viewpoints) without doing some feature extraction/keypoint detection first. For example, open-source plug-and-play 2D keypoint detectors are extremely good nowadays (e.g HRNet), and explicitly detecting 2D keypoints before classifying deadlift videos as "good" or "bad" will likely make the task more feasible!
Related posts
- [D] HRNET- Human Pose Estimation [Implementation]
- Real time 3d reconstruction with known parameters of cameras and environment?
- Auto tagging Images ( particularly for p0rn ) scene-wise
- AvatarPoser - full body pose tracking from nothing but the 6D input of headset and controllers or hands
- Any ghetto Mo'Cap solutions?