Deep3DFaceRecon_pytorch: high reconstruct error on now challenge

Thanks for the work, it is very helpful. But I got some problem on test for now challenge. I tested the pretrained model that you supply on Now challenge and got much higher error on validation part of Now challenge.

(deep3d_pytroch_official_nexp - median: 1.286424, mean: 1.864963, std: 2.361429)

• I use MTCNN to detect 5 landmarks for every face in Now challenge.

• Align the face to the template mesh as you did in test.py

def read_data(im_path, lm_path, lm3d_std, to_tensor=True):
    # to RGB 
    im = Image.open(im_path).convert('RGB')
    W,H = im.size
    lm = np.loadtxt(lm_path).astype(np.float32)
    lm = lm.reshape([-1, 2])
    lm[:, -1] = H - 1 - lm[:, -1]
    _, im, lm, _ = align_img(im, lm, lm3d_std)
    if to_tensor:
        im = torch.tensor(np.array(im)/255., dtype=torch.float32).permute(2, 0, 1).unsqueeze(0)
        lm = torch.tensor(lm).unsqueeze(0)
    return im, lm

• Also，I set exp and angle as 0 to produce netural face mesh for every picture.

    coef_dict = self.split_coeff(coeffs)
    ## kj add 
    coef_dict ['exp'][:] = 0.
    coef_dict['angle'][:]= 0.
    face_shape = self.compute_shape(coef_dict['id'], coef_dict['exp'])
    rotation = self.compute_rotation(coef_dict['angle'])

• As for 7 3d landmarks metioned in Now challenge , I selected points like this:

    recon_shape = self.pred_vertex  # get reconstructed shape
    # print("recon_shape2:",recon_shape)
    # recon_shape[..., -1] = 10 - recon_shape[..., -1] # from camera space to world space  #save mesh part have already transformed shape to  world space 
    recon_shape = recon_shape.cpu().numpy()[0]
    lm_3d_indx = self.facemodel.keypoints.cpu().numpy()[[36,39,42,45,33,48,54]]
    lm_3d = recon_shape[lm_3d_indx,:]

I hope that you could point out the which step is wrong or what i missed , I would really apreciate it. It would be great if you could provide the evaluation code for Now challange.