Code about calculating LVE

[WASD4959]

Hi, when I reading codes about calculating metrics in cal_metric.py, I have some confusions. The defination of LVE in the paper is:

• calculating the maximal L2 error of all lip vertices for each frame and takes the average over all frames.

But the codes seems to calculate the maximal L2 error of all frames for each lip vertices and takes the average over all lip vertices.

    vertices_gt_all = np.array(vertices_gt_all)
    vertices_pred_all = np.array(vertices_pred_all)

    L2_dis_mouth_max = np.array([np.square(vertices_gt_all[:,v, :]-vertices_pred_all[:,v,:]) for v in mouth_map])
    L2_dis_mouth_max = np.transpose(L2_dis_mouth_max, (1,0,2))
    L2_dis_mouth_max = np.sum(L2_dis_mouth_max,axis=2)
    L2_dis_mouth_max = np.max(L2_dis_mouth_max,axis=1)

    print('Lip Vertex Error: {:.4e}'.format(np.mean(L2_dis_mouth_max)))

This really makes me feel confused. Did I misunderstand LVE ? Can you help me solve my confusion, Thank you!

[Doubiiu]

Hi I think you misunderstood the code. It should be consistent with the description in the paper. I add some comments here for your better understanding.

    L2_dis_mouth_max = np.array([np.square(vertices_gt_all[:,v, :]-vertices_pred_all[:,v,:]) for v in mouth_map]) #(mouth_vertices,frames,3) Firstly, this tensor should be a list of (frames, 3) and then converted into nparray (mouth_vertices,frames,3)
    L2_dis_mouth_max = np.transpose(L2_dis_mouth_max, (1,0,2)) #(frames, mouth_vertices,3)
    L2_dis_mouth_max = np.sum(L2_dis_mouth_max,axis=2) #(frames, mouth_vertices,)
    L2_dis_mouth_max = np.max(L2_dis_mouth_max,axis=1)#(frames, )

You can run the code to check if the shape is correct.

[WASD4959]

Sorry I misunderstood the code.Thank you for your kindly reply! :D