Awesome
Benchmark and Evaluation of "Efficient Emotional Adaptation for Audio-Driven Talking-Head Generation"
Setup
Before beginning, ensure your environment aligns with the EAT repository requirements. Install the necessary package:
pip install face-alignment
Evaluation Instructions
-
Download Pre-trained Models:
-
Access the pre-trained models from this link.
-
After downloading, unzip the files and place them into the
code
folder.unzip code.zip -d code
-
-
Download Ground Truth Videos:
-
Obtain the cropped Ground Truth videos from this link.
-
Once downloaded, unzip the files and move them into the root directory with the command:
unzip talking_head_testing.zip -d talking_head_testing
-
-
Place Your Results:
- Position your evaluation results in the
./result
folder.
- Position your evaluation results in the
-
Execution:
Evaluate MEAD
- For instance, if your sampled (100) test results are located in the folder
./result/deepprompt_eam3d_all_final_313
, execute the following bash command:
bash test_psnr_ssim_sync_emoacc.sh deepprompt_eam3d_all_final_313 0
- If you want to test the whole 985 results in MEAD test set, execute the following bash command:
bash test_psnr_ssim_sync_emoacc_985.sh deepprompt_eam3d_all_final_313_985 0
Evaluate LRW
- Different from MEAD, you need to download LRW dataset from here.
- We only use the testset of LRW.
- Extract the wav with
python extract_wav.py
- Align and crop with PCAVS preprocess:
bash preprocess_lrw_gt.sh '[VIDEO_PATH]'
. Replace[VIDEO_PATH]
with the absolute folder path of dataset videos, eg. '/data/lipread_test_25/video'. - Put your test result of EAT into './result'
- Change the name in test_psnr_ssim_sync_lrw_100.sh. Run
bash test_psnr_ssim_sync_lrw_100.sh
to test LRW with 100 samples. - Change the name in test_psnr_ssim_sync_lrw_25k.sh. Run
bash test_psnr_ssim_sync_lrw_25k.sh
to test LRW with all 25k samples.
- For instance, if your sampled (100) test results are located in the folder