This is the code of this ICIG2023 paper.
To download the dataset of this work, please refer to the DFGC-2022 project and apply with an application form.The downloaded dataset is assumed to be saved in the data folder.
The labels used in this paper can be found in the metadata folder. The C*-mos files provide the ground truth realism score of each video, while the C*-mos-subid files provide a method level realism score by averaging the scores of all videos with the same submission id, as each submission id repersents a unique face-swap method.
For video preprocessing, we crop the videos and keep only the facial region. Relative coordinates of the bounding box for each video is provided in the crop folder, with the top left corner set as the origin. x and y denote the location of the center point of the bounding box. Note that the width w and height h of the boxes in the given files are the original face detection results given by cv2.CascadeClassifier, so you will need to crop the image using a 1.3 times larger box (w*1.3 × h*1.3) to reproduce the experiment in the paper.
We borrowed models from related tasks as our feature extractors. You may find these models according to the table below.
For the traditional-feature-based models, you can get the features with modifying a few paths in their original code. For the deep models, we provide the feature extraction code in the extra_feature folder. You need to download the models first and put it in the folder with the corrseponding name.
All extracted features are assumed to be saved in the feature folder.
| method | link |
|---|---|
| BRISQUE | matlab |
| GM-LOG | matlab |
| FRIQUEE | matlab |
| TLVQM | matlab |
| V-BLIINDS | pytorch |
| VIDEVAL | matlab |
| RAPIQUE | matlab |
| ResNet | Provided by torchvision |
| VGGFace | pytorch |
| DFDC-ispl | pytorch |
| DFGC-1st | pytorch |
We first decide the dimensionality of the selected features by running feats_num_select.py, then perform the selection by running feats_select.py.
We train a SVR on the selected features and test its performance. Run predict.py to get a prediction, run eval.py for evaluation. Please refer to the code for more details.
This scrip is mostly borrowed from here.
If you find our work useful, please cite it as:
@article{sun2023visual,
title={Visual Realism Assessment for Face-swap Videos},
author={Sun, Xianyun and Dong, Beibei and Wang, Caiyong and Peng, Bo and Dong, Jing},
journal={arXiv preprint arXiv:2302.00918},
year={2023}
}