Hi @yihuacheng, I trained your pre-trained model on MPIIFaceGaze. I haven't made any changes in the script for training as well as pre-processing of dataset. I performed the leave-one-person-out evaluation on this dataset as mentioned in your paper. I am using PyTorch 1.7.0. I got the following best angular errors for respective person:

Person | Best error -- | -- 0 | 2.37 1 | 4.36 2 | 4.41 3 | 4.49 4 | 3.05 5 | 3.79 6 | 3.07 7 | 4.34 8 | 4.44 9 | 4.15 10 | 5.89 11 | 5.42 12 | 4.09 13 | 3.71 14 | 6.23 Mean | 4.254

The mean of this best angular errors comes out to be 4.254, which is far away from the reported 4.00 error. Please let me know if I am missing something over here. Also, help me to reproduce the reported results.

Thank you for your great job! I trained your pre-trained model on RT-Gene. But i find the result is far away from the result of paper,the result is about 13, I used the face image as input to get the gaze angle directly. I want to know how to train on RT-Gene in your trial.

Hi, yihuacheng. I am too poor to train the model of pure ViT using EHT-XGaze. But I want to know the error of ETH-XGaze dataset using pure ViT model. Can you share the Pre-trained pure ViT model using EHT-XGaze? Thank you very much!!!

Hi, Yihua, thanks for the great job regarding appearance-based gaze-estimation. I have gone through the review paper and codes in Gazehub. It seems that the way to acquireing Rs and Ts from CCS to SCS is not mentioned. It would be appreciated if you could elaborate a bit more on that or provide any reference link or paper. Thanks in advance.

Hi,

I have some problems about the MPII dataset processing and the use of MPII data in the GazeTR model.

In GazeTR reader.py, you define the decode function for MPII like this:

def Decode_MPII(line):
    anno = edict()
    anno.face, anno.lefteye, anno.righteye = line[0], line[1], line[2]
    anno.name = line[3]

    anno.gaze3d, anno.head3d = line[5], line[6]
    anno.gaze2d, anno.head2d = line[7], line[8]
    return anno

And in the data_processing_mpii.py file you provided, you process and write the annotations into the format like this:

outfile.write("Face Left Right Grid Origin whicheye 2DPoint HeadRot HeadTrans ratio FaceCorner LeftEyeCorner RightEyeCorner\n")

If you indeed used the same code to process the MPII dataset for training the GazeTR model, then this is not right. You can see that anno.gaze2d = line[7] which is actaully corresponding to HeadRot.

Could you please give some explanation about how to correctly use the data_processing code and how to load the data in GazeTR?

Thank you very much and best regards

I think many of the gaze estimation related works misunderstand the term.

0. yaw pitch is different from spherical coordinate, so your function is wrong
1. most of the dataset's gaze3d label is from camera's coordinate, so you can't just transform your output to 3d and then calculate the arccos