Motion Attetnion Evaluation

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This Motion Attention Evaluation aims to draw the attention matrix and attention nodes on human skeletal.

• The goal of this attention matrix is to find some potential relationships between two joints which include adjacent joints and non-adjacent joints. In our case, we only show the top-3 important relationshop between joints.
• The meaning of the attention nodes can show the importance of top 3 nodes.
• Because we set 4 attention matrix on our training setting. There will only show 4 attention matrix.

The value of the importance Top 3

• top3 $\to$ top1

• $\to$ $\to$ Attention Matrix $M_0$ : Use Reverse COLORMAP_SPRING

• $\to$ $\to$ Attention Matrix $M_1$ : Use COLORMAP_SUMMER

• $\to$ $\to$ Attention Matrix $M_2$ : Use COLORMAP_COOL

• $\to$ $\to$ Attention Matrix $M_3$ : Use Reverse COLORMAP_AUTUMN

ColorMap is referenced by https://docs.opencv.org/4.x/d3/d50/group__imgproc__colormap.html

Visualize Attention Matrix and Attention Node on human skeleton

Prepare

Prepare the alphapose skeleton and the video

dir_path : '/home/{$USER}/datasets/Skating_Dataset

Take Loop for example, the directory path should be

- Skating_Dataset
    - alpha_pose_{$FILENAME}
        - vis 
            - 0.jpg
            - 1.jpg
                ...
        - {$FILENAME}.mp4
        - {$FILENAME}.npz
        - alphapose-results.json
    - alpha_pose_{$FILENAME}
        ...

Prepare the config file

# finetune_nodiff_andrew
attention_node : '{$USER}/MotionExpert/results/finetune_nodiff_andrew/jsons/att_node_results_epoch90.json'
attention_matrix : '{$USER}/MotionExpert/results/finetune_nodiff_andrew/jsons/att_A_results_epoch90.json'
epoch_num : 90
output_dir : '{$USER}/Evaluation/finetuneAttention'
video_dir : '{$USER}/datasets/Skating_Dataset0811'
results_epoch : '{$USER}/MotionExpert/results/finetune_nodiff_andrew/jsons/results_epoch90.json'

Run

$ conda activate motion2text
$ python draw_skeleton2D.py /home/weihsin/projects/Evaluation/config_file/finetuneAttention.yaml 

Implement Detailed

Originally, there are 22 joints on SMPL format. I map the SMPL format to OpenPose format that Alphapose use.

• SMPL format
• OpenPose format
• Mapping SMPL $\to$ OpenPose

mapping = [-1, 12, 15, -1, -1, 16, 17, 18, 19, 20, 21, 1, 2, 4, 5, 7, 8]

SMPL format             Open Pose     Map     SMPL
 0 : pelvis              0 : Nose                  (None)
 1 : left-hip            1 : LEye             12 : neck 
 2 : right-hip           2 : REye             15 : head  
 3 : spine-1             3 : LEar                  (None)
 4 : left-knee           4 : REar                  (None)
 5 : right-knee          5 : LShoulder        16 : left-shoulder
 6 : spine-2             6 : RShoulder        17 : right-shoulder
 7 : left-ankle          7 : LElbow           18 : left-elbow
 8 : right-ankle         8 : RElbow           19 : right-elbow
 9 : spine-3             9 : LWrist           20 : left-wrist
10 : left-foot          10 : RWrist           21 : right-wrist
11 : right-foot         11 : LHip              1 : left-hip
12 : neck               12 : RHip              2 : right-hip
13 : left-collar        13 : LKnee             4 : left-knee 
14 : right-collar       14 : Rknee             5 : right-knee   
15 : head               15 : LAnkle            7 : left-ankle 
16 : left-shoulder      16 : RAnkle            8 : right-ankle 
17 : right-shoulder 
18 : left-elbow
19 : right-elbow
20 : left-wrist
21 : right-wrist

OpenPose format is reference from https://github.com/MVIG-SJTU/AlphaPose/blob/master/docs/output.md