datasets_evaluation.md

Download datasets

Here are the links to download the datasets FaceForensics, SkyTimelapse, UCF101, and Taichi-HD.

Dataset structure

All datasets follow their original dataset structure. As for video-image joint training, there is a train_list.txt file, whose format is video_name/frame.jpg. Here, we show an example of the FaceForensics datsset.

All datasets retain their original structure. For video-image joint training, there is a train_list.txt file formatted as video_name/frame.jpg. Below is an example from the FaceForensics dataset.

aS62n5PdTIU_1_8WGsQ0Y7uyU_1/000306.jpg
aS62n5PdTIU_1_8WGsQ0Y7uyU_1/000111.jpg
aS62n5PdTIU_1_8WGsQ0Y7uyU_1/000007.jpg
aS62n5PdTIU_1_8WGsQ0Y7uyU_1/000057.jpg
aS62n5PdTIU_1_8WGsQ0Y7uyU_1/000084.jpg
aS62n5PdTIU_1_8WGsQ0Y7uyU_1/000268.jpg
aS62n5PdTIU_1_8WGsQ0Y7uyU_1/000270.jpg
aS62n5PdTIU_1_8WGsQ0Y7uyU_1/000259.jpg
aS62n5PdTIU_1_8WGsQ0Y7uyU_1/000127.jpg
aS62n5PdTIU_1_8WGsQ0Y7uyU_1/000099.jpg
aS62n5PdTIU_1_8WGsQ0Y7uyU_1/000189.jpg
aS62n5PdTIU_1_8WGsQ0Y7uyU_1/000228.jpg
aS62n5PdTIU_1_8WGsQ0Y7uyU_1/000026.jpg
aS62n5PdTIU_1_8WGsQ0Y7uyU_1/000081.jpg
aS62n5PdTIU_1_8WGsQ0Y7uyU_1/000094.jpg
aS62n5PdTIU_1_8WGsQ0Y7uyU_1/000223.jpg
aS62n5PdTIU_1_8WGsQ0Y7uyU_1/000055.jpg
qEnKi82wWgE_2_rJPM8EdWShs_1/000486.jpg
qEnKi82wWgE_2_rJPM8EdWShs_1/000396.jpg
qEnKi82wWgE_2_rJPM8EdWShs_1/000475.jpg
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qEnKi82wWgE_2_rJPM8EdWShs_1/000261.jpg
qEnKi82wWgE_2_rJPM8EdWShs_1/000294.jpg
qEnKi82wWgE_2_rJPM8EdWShs_1/000257.jpg
qEnKi82wWgE_2_rJPM8EdWShs_1/000490.jpg
qEnKi82wWgE_2_rJPM8EdWShs_1/000143.jpg
qEnKi82wWgE_2_rJPM8EdWShs_1/000190.jpg
qEnKi82wWgE_2_rJPM8EdWShs_1/000476.jpg
qEnKi82wWgE_2_rJPM8EdWShs_1/000397.jpg
qEnKi82wWgE_2_rJPM8EdWShs_1/000437.jpg
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Evaluation

We follow StyleGAN-V to measure the quality of the generated video. The code for calculating the relevant metrics is located in tools folder. To measure the quantitative metrics of your generated results, you need to put all the videos from real data into a folder, turn them into video frames and do center-crop-resize-256 (the same goes for fake data). Then you can run the following command on one GPU:

# cd Latte
bash tools/eval_metrics.sh