Methods for human pose estimation is a review of research papers that solve the problem of Human Pose Estimation in 2D/3D writen in Croatian. Classical approaches and deep learning approaches were explored and described.
Deep learning methods were divided into groups based on the type of arhitecture and the type of approach used for estimating poses.
Architectures that were reviewed are:
- Convolutional architectures
- Attention architectures
- Graph architectures
Approaches for 2D estimation that were reviewed:
- Single stage approaches
- Multi-stage approaches
Approaches for 3D estimation that were reviewed:
- Single view approach
- direct regression
- 2D-to-3D lifting
- Multi view approach
Currently best models for human pose prediction according to Paper With Code were described and two methods were implemented and evaluated on Human3.6M validation dataset.