REC-MV_preprocess

This is the tutorial of data processing of REC-MV.

The data pre-processing part includes img, mask, normal, parsing (garment segmentation), camera, smpl parameters (beta & theta), featurelines, skinning weight.

Step0

set up the environment (or you can directly use REC-MV environment)

pip install -r requirements.txt

Step1

You should make directory to save all processed data, named, to say, xiaoming. And you turn the video into images:

encodepngffmpeg()
{
	# $1: target folder
	# $2: save video name
    ffmpeg -r ${1} -pattern_type glob -i '*.png' -vcodec libx264 -crf 18 -vf "pad=ceil(iw/2)*2:ceil(ih/2)*2" -pix_fmt yuv420p ${2}
}


encodepngffmpeg 30 ./xiaoming.mp4

Then, your data directory:

xiaoming/
└── imgs

Step2 Normal, Parsing, and Mask

Get the normal map, parsing mask, masks.

python prcess_data_all.py --gid <gpu_id> --root <Your data root> --gender <data gender>
# example
python prcess_data_all.py --gid 0 --root /data/xiaoming --gender male 

Your data directory:

xiaoming/
├── imgs
├── masks
├── normals
└── parsing_SCH_ATR

Step3 SMPL & Camera

To get smpl paramaters (pose and shape), here we use videoavatar:

• Set up the env (Note it use python2)
• Prepare keypoints files for each frame in the video and put them under xiaoming/openpose, which I use Openpose.
• Run three python files in videoavatars/prepare_data, you'll get keypoints.hdf5, masks.hdf5, camera.hdf5. Or you can just use my script: cd videoavatars; python get_reconstructed_poses.py --root xiaoming --out xiaoming --gender male
• bash run_step1.sh

After you run through videoavatar, you will get camera.pkl, reconstructed_poses.hdf5. Put it also under the root(xiaoming).

You can get smpl_rec.npz, camera.npz by running:

python get_smpl_rec_camera.py --root xiaoming --save_root xiaoming --gender male

Note: You can use any other smpl estimation algorithm, but you should follow the way how smpl_rec.npz save pose, shape, and trans.

Step4 Skining Weight

We follow fite to get the lbs skinning weight to prevent artifacts.

In fite's readme, you'll get a skining weight cube after finishing 3.Diffused Skinning. Name it diffused_skinning_weights.npy and put it under xiaoming.