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To organize the checkpoints, we first download the data from the provided link - Google Drive
For example, I want to evaluate on the data and pretrained checkpoint of obama.
Then we run the following commands:
cd SplattingAvatar
mkdir results
cd results
put your obama.7z under SplattingAvatar/results/obama.7z
unzip the 7z file
7za x obama.7z
rm -rf obama.7z
move the flame_params.json file, move the /images folder and rename it as /image
We use male-3-casual as an example:
download the peopleSnapShot data from PeopleSnapshot, unzip it and you can find a folder named male-3-casual
download the pretrained weights from Google Drive, unzip it and you can find a folder also named male-3-casual
merge the contents in the two male-3-casual folders mentioned above, and put the new male-3-casual folder under the directory results/male-3-casual
landmark numpy is missing:
The authors didn't provide the landmark_embedding.npy, and I finally find it on https://github.com/yfeng95/DECA/tree/master/data
you can download it from this weblink
Render the face avatar images:
The authors of this repository made a mistake, when you run the eval_splatting_avatar.py, you should organize the checkpoints as follows:
/output-splatting/last_checkpoint
|---eval_30000
|---image
|---point_cloud
|---config.yaml
|---flame_params.json
To organize the checkpoints, we first download the data from the provided link - Google Drive
For example, I want to evaluate on the data and pretrained checkpoint of obama.
Then we run the following commands:
put your obama.7z under SplattingAvatar/results/obama.7z
unzip the 7z file
move the flame_params.json file, move the /images folder and rename it as /image
then go back to the folder /SplattingAvatar
Finally, you should run the command as follows:
You will get the rendered results in the folder:
results/obama/output-splatting/last_checkpoint/point_cloud/iteration_30000/eval/render
if you want to acquire the rendered images with the pose in training dataset:
change the sentence in eval_splatting_avatar.py from
to
Render the full-body avatar images:
We use male-3-casual as an example:
download the peopleSnapShot data from PeopleSnapshot, unzip it and you can find a folder named male-3-casual
download the pretrained weights from Google Drive, unzip it and you can find a folder also named male-3-casual
merge the contents in the two male-3-casual folders mentioned above, and put the new male-3-casual folder under the directory results/male-3-casual
then preprocess
then move the config.json
render:
Find the rendered images in
results/male-3-casual/output-splatting/last_checkpoint/point_cloud/iteration_30000/eval/render
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