1. rgh

2. Motivation
Spatial contextual correlation among different joints plays an important role in human pose estimation.
Since human pose estimation is related to structure, it is important to design appropriate guideline to choose directions of information propagation for the joints that are unclear or occluded.

3. Framework

4. Cascade Prediction Fusion (CPF)
Predi = P( Conv(Predi-1)+Feat )

5. Pose Graph Neural Network(PGNN)
Graph Construction
The hidden state of each node is initialized with its corresponding spatial prediction feature map derived from the original image.

6. Review GRU vs LSTM 1. Add reset gate
2. Input gate and forget gate -> update gate
3. Cell and hidden -> hidden
4. Remove output gate
convolutional layers can be used as geometrical transform kernels.
Structured Feature Learning for Pose Estimation---Xiao Chu

7. Information Propagation
GRU+GNN
It is noted that the weights of convolution for different edges are not shared

8. Experiments-Ablation Study

9. Experiments

10. Other Analysis